Persistent network model diagnostics - balanced statistics
This file shows diagnostics for persistent network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced mixing matrices. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
library("here")## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMPload(file = here("Model fits and simulations/Fit tests and debugging/est/fit.p.buildup.bal.rda"))Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 |
| nodefactor.deg.main.1 | NA | NA | NA | 1699.0 | 1699.0 | 1699.0 | 1699.0 | 1699.0 |
| nodefactor.race..wa.B | NA | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 |
| nodefactor.race..wa.H | NA | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 |
| nodefactor.region.EW | NA | NA | NA | NA | 367.6 | 367.6 | 367.6 | 367.6 |
| nodefactor.region.OW | NA | NA | NA | NA | 1182.3 | 1182.3 | 1182.3 | 1182.3 |
| concurrent | NA | NA | NA | NA | NA | NA | 1384.0 | 1384.0 |
| nodematch.race..wa.B | NA | NA | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 |
| nodematch.race..wa.H | NA | NA | 51.2 | 51.2 | 51.2 | 51.2 | 51.2 | 51.2 |
| nodematch.race..wa.O | NA | NA | 1247.1 | 1247.1 | 1247.1 | 1247.1 | 1247.1 | 1247.1 |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 1614.0 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 1664.8 | 1664.8 | 1664.8 |
| degrange | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
The control settings for these models are:
set.control.ergm = ccontrol.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## -0.2693 40.4159 0.2333 0.2324
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -78.5 -27.5 -0.5 26.5 78.5
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.00000000
## Lag 1e+05 -0.01264310
## Lag 2e+05 0.02340828
## Lag 3e+05 -0.00426963
## Lag 4e+05 -0.01833374
## Lag 5e+05 -0.02891705
## Chain 2
## edges
## Lag 0 1.0000000000
## Lag 1e+05 0.0097286168
## Lag 2e+05 -0.0004894332
## Lag 3e+05 0.0142298259
## Lag 4e+05 0.0077479447
## Lag 5e+05 0.0153968890
## Chain 3
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.017667766
## Lag 2e+05 0.013800249
## Lag 3e+05 0.005901856
## Lag 4e+05 0.002580839
## Lag 5e+05 0.003736537
## Chain 4
## edges
## Lag 0 1.00000000
## Lag 1e+05 0.01953609
## Lag 2e+05 0.01789521
## Lag 3e+05 -0.01244918
## Lag 4e+05 -0.02167088
## Lag 5e+05 0.01275054
## Chain 5
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0055063925
## Lag 2e+05 0.0083421464
## Lag 3e+05 -0.0131939117
## Lag 4e+05 -0.0001138946
## Lag 5e+05 0.0158752099
## Chain 6
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.018427988
## Lag 2e+05 0.008982739
## Lag 3e+05 -0.030548372
## Lag 4e+05 0.018199035
## Lag 5e+05 -0.006587675
## Chain 7
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.034409587
## Lag 2e+05 0.017008519
## Lag 3e+05 -0.016041825
## Lag 4e+05 -0.006265774
## Lag 5e+05 0.014039680
## Chain 8
## edges
## Lag 0 1.00000000
## Lag 1e+05 0.01156856
## Lag 2e+05 0.02465699
## Lag 3e+05 0.01027490
## Lag 4e+05 -0.01866472
## Lag 5e+05 0.00814208
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.2032
##
## Individual P-values (lower = worse):
## edges
## 0.8389906
## Joint P-value (lower = worse): 0.8345032 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.4927
##
## Individual P-values (lower = worse):
## edges
## 0.6222378
## Joint P-value (lower = worse): 0.6365891 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.3314
##
## Individual P-values (lower = worse):
## edges
## 0.7403632
## Joint P-value (lower = worse): 0.7407062 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.0489
##
## Individual P-values (lower = worse):
## edges
## 0.9609959
## Joint P-value (lower = worse): 0.9610362 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.481
##
## Individual P-values (lower = worse):
## edges
## 0.1385718
## Joint P-value (lower = worse): 0.1321135 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.746
##
## Individual P-values (lower = worse):
## edges
## 0.0808636
## Joint P-value (lower = worse): 0.08687598 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.6178
##
## Individual P-values (lower = worse):
## edges
## 0.5367057
## Joint P-value (lower = worse): 0.511578 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.1803
##
## Individual P-values (lower = worse):
## edges
## 0.8569124
## Joint P-value (lower = worse): 0.8625581 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.09110 39.98 0.23084 0.22820
## nodefactor.race..wa.B 0.07903 16.15 0.09322 0.09365
## nodefactor.race..wa.H 0.68867 23.45 0.13537 0.13636
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -78.50 -26.50 -0.5000 26.50 78.50
## nodefactor.race..wa.B -31.52 -10.52 0.4832 10.48 32.48
## nodefactor.race..wa.H -44.34 -15.34 0.6600 16.66 46.66
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.34043850
## nodefactor.race..wa.B 0.3404385 1.00000000
## nodefactor.race..wa.H 0.4714240 0.07104756
## nodefactor.race..wa.H
## edges 0.47142397
## nodefactor.race..wa.B 0.07104756
## nodefactor.race..wa.H 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.014056138 0.002684782 -0.009837923
## Lag 2e+05 0.009601482 0.027872754 -0.006658632
## Lag 3e+05 -0.021384550 0.016004081 -0.003078447
## Lag 4e+05 0.020746657 0.011123528 0.018909300
## Lag 5e+05 -0.031688277 0.021454748 0.006354803
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.015375920 0.013907793 0.005285046
## Lag 2e+05 0.004422912 0.010542225 -0.010488125
## Lag 3e+05 0.008273100 -0.021453347 -0.003996662
## Lag 4e+05 -0.020135105 0.014140349 -0.018196243
## Lag 5e+05 0.016189235 -0.009558596 0.002314518
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.008418285 0.020811394 -0.02164845
## Lag 2e+05 0.007272971 0.014815020 -0.01199940
## Lag 3e+05 -0.011430331 0.006676682 0.02394959
## Lag 4e+05 0.013043680 -0.026303588 0.01219512
## Lag 5e+05 -0.008226237 -0.019227916 -0.02001891
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.001047007 0.007165719 0.004493936
## Lag 2e+05 -0.002856752 -0.004022528 0.011372678
## Lag 3e+05 -0.004367930 0.016923844 -0.012796269
## Lag 4e+05 -0.015947214 -0.004892327 0.001331095
## Lag 5e+05 -0.019503067 -0.016475032 0.022703284
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.007100868 0.036214870 0.006731455
## Lag 2e+05 0.023051907 0.013640057 0.019028848
## Lag 3e+05 -0.016022507 -0.002459983 -0.017163470
## Lag 4e+05 -0.003299190 0.019133762 -0.026224077
## Lag 5e+05 0.008201155 -0.015749485 0.040439013
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008263184 0.001980644 0.010036553
## Lag 2e+05 -0.008049266 -0.014203663 0.019661007
## Lag 3e+05 0.002443602 0.008704505 0.016815173
## Lag 4e+05 -0.036731424 -0.011608567 0.001098624
## Lag 5e+05 0.021488107 -0.006153972 0.007991003
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.004780772 0.006810960 -0.016046380
## Lag 2e+05 -0.001116000 -0.004969817 -0.012715600
## Lag 3e+05 -0.006197987 -0.019398210 0.002192296
## Lag 4e+05 -0.027419983 -0.002447443 -0.007998861
## Lag 5e+05 0.005475253 0.005611519 -0.004733637
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.025949984 0.020703104 -0.004187630
## Lag 2e+05 -0.009835624 0.010510133 -0.002304295
## Lag 3e+05 -0.006874789 0.004327962 -0.002507147
## Lag 4e+05 -0.021181794 0.009414561 -0.036419091
## Lag 5e+05 0.033435427 0.041390937 0.014618093
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.6982 -0.0121 -0.9571
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4850279 0.9903456 0.3385000
## Joint P-value (lower = worse): 0.790269 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.0060 0.8227 -0.2323
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3143990 0.4106945 0.8162875
## Joint P-value (lower = worse): 0.6425545 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.7328 -0.6441 -0.3608
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4636517 0.5194926 0.7182590
## Joint P-value (lower = worse): 0.8784429 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.03504 0.79803 -0.75937
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9720451 0.4248510 0.4476300
## Joint P-value (lower = worse): 0.7355735 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.07216 -0.62653 -0.43441
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9424724 0.5309689 0.6639910
## Joint P-value (lower = worse): 0.8647927 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4155 0.6445 0.2857
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6777416 0.5192281 0.7751256
## Joint P-value (lower = worse): 0.929725 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.2834 0.1738 -1.6865
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.19936280 0.86201320 0.09170284
## Joint P-value (lower = worse): 0.2793509 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -2.6602 -0.8513 -1.4327
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.007810195 0.394610098 0.151944926
## Joint P-value (lower = worse): 0.08011385 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.49160 40.392 0.23320 0.22875
## nodefactor.race..wa.B 0.10247 16.100 0.09295 0.09439
## nodefactor.race..wa.H -0.64150 23.741 0.13707 0.13574
## nodematch.race..wa.B 0.03895 2.903 0.01676 0.01672
## nodematch.race..wa.H -0.08816 6.900 0.03984 0.03983
## nodematch.race..wa.O -0.01568 32.784 0.18928 0.18931
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -78.50 -27.500 -0.50000 26.500 79.50
## nodefactor.race..wa.B -30.52 -10.517 -0.51680 10.483 32.48
## nodefactor.race..wa.H -46.34 -17.340 -0.34000 15.660 45.66
## nodematch.race..wa.B -5.48 -2.480 -0.47985 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.18150 4.819 13.82
## nodematch.race..wa.O -63.08 -22.081 -0.08078 21.919 64.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.35032349
## nodefactor.race..wa.B 0.3503235 1.00000000
## nodefactor.race..wa.H 0.4731012 0.10951385
## nodematch.race..wa.B 0.0510416 0.31164161
## nodematch.race..wa.H 0.1251259 -0.01310216
## nodematch.race..wa.O 0.7840966 -0.01465899
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.47310122 0.051041602
## nodefactor.race..wa.B 0.10951385 0.311641607
## nodefactor.race..wa.H 1.00000000 -0.021040693
## nodematch.race..wa.B -0.02104069 1.000000000
## nodematch.race..wa.H 0.49246973 -0.005351267
## nodematch.race..wa.O -0.03079702 0.007037903
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.125125858 0.784096612
## nodefactor.race..wa.B -0.013102161 -0.014658986
## nodefactor.race..wa.H 0.492469731 -0.030797024
## nodematch.race..wa.B -0.005351267 0.007037903
## nodematch.race..wa.H 1.000000000 0.004971174
## nodematch.race..wa.O 0.004971174 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.004194474 0.0075914377 -0.012642645
## Lag 2e+05 0.001189938 -0.0077760006 -0.013051100
## Lag 3e+05 -0.013498475 -0.0015569134 -0.009593246
## Lag 4e+05 -0.028407617 -0.0002640455 -0.004609579
## Lag 5e+05 0.023342365 0.0074494724 0.020201513
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.017936373 -0.004130326 -0.001240103
## Lag 2e+05 -0.011342263 -0.007115653 0.011922060
## Lag 3e+05 0.008160085 -0.028034668 0.004731922
## Lag 4e+05 0.003387611 0.013319112 -0.008112927
## Lag 5e+05 0.000192203 0.001983806 0.035126530
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0155963242 0.005599207 -0.0065676747
## Lag 2e+05 -0.0056243883 0.007062517 0.0020552492
## Lag 3e+05 -0.0033827699 -0.005364336 -0.0172668074
## Lag 4e+05 -0.0222674387 0.030549916 0.0005323911
## Lag 5e+05 0.0007441259 -0.005040395 0.0069328232
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.01325389 0.021283659 -0.002475173
## Lag 2e+05 0.01487761 0.022805977 -0.013027309
## Lag 3e+05 0.02235377 -0.006504199 0.027756411
## Lag 4e+05 -0.04650066 -0.005498602 -0.013712560
## Lag 5e+05 0.01115581 -0.002193962 -0.004388494
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.0000000000 1.00000000
## Lag 1e+05 0.0011042129 -0.0034361775 -0.02295850
## Lag 2e+05 0.0003558665 -0.0135148853 0.01457583
## Lag 3e+05 0.0257859960 0.0067996675 -0.03581431
## Lag 4e+05 0.0221185423 0.0002173176 0.02190516
## Lag 5e+05 -0.0159792426 0.0067314282 -0.03160936
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.00184724 -0.0154134135 -0.012907295
## Lag 2e+05 -0.01540586 0.0055993066 0.002586412
## Lag 3e+05 0.02086939 -0.0102281941 0.011847935
## Lag 4e+05 -0.01260758 -0.0037541334 -0.008167550
## Lag 5e+05 -0.01998848 0.0005672827 -0.004071502
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000e+00 1.000000000
## Lag 1e+05 0.0224645616 1.784195e-02 0.010455881
## Lag 2e+05 0.0089836522 4.040036e-02 -0.008024200
## Lag 3e+05 -0.0141784233 -8.261718e-05 -0.003862752
## Lag 4e+05 -0.0091492865 2.992907e-02 0.003487751
## Lag 5e+05 0.0002028272 -3.524313e-03 -0.001226870
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.016053619 0.016979932 0.004451795
## Lag 2e+05 -0.028064110 -0.014351816 0.014273858
## Lag 3e+05 0.002085090 -0.003588906 -0.004309958
## Lag 4e+05 -0.001016541 -0.010506408 0.004567370
## Lag 5e+05 -0.028698303 -0.040388134 0.026708334
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.007854902 0.011695396 -0.012191540
## Lag 2e+05 -0.005259711 -0.013199314 -0.000860708
## Lag 3e+05 0.023212640 0.020899059 0.001762955
## Lag 4e+05 0.034799520 0.039169651 0.003499383
## Lag 5e+05 0.008316642 -0.005131585 0.041519582
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.019129467 0.013971358 0.014335328
## Lag 2e+05 -0.002905375 0.009345281 -0.011161619
## Lag 3e+05 0.013839668 0.017660618 -0.005213653
## Lag 4e+05 0.022730103 0.018097662 0.010710719
## Lag 5e+05 -0.015218436 0.002986982 0.008519737
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.0081564273 0.0087633288 0.0070437959
## Lag 2e+05 0.0007834107 -0.0067433259 0.0149598041
## Lag 3e+05 -0.0134839361 0.0081827626 0.0006985165
## Lag 4e+05 -0.0067049282 -0.0009645366 -0.0017836691
## Lag 5e+05 -0.0152227996 -0.0036686520 -0.0209157048
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.007473357 -0.013586701 0.002125045
## Lag 2e+05 -0.003967014 -0.010837192 0.011437851
## Lag 3e+05 -0.004074421 0.001415328 -0.007356995
## Lag 4e+05 0.026260508 -0.005507372 -0.003251000
## Lag 5e+05 -0.028163935 0.008707329 -0.021063262
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.033540290 -0.0153248465 -0.009173664
## Lag 2e+05 -0.016990173 -0.0008163169 0.006012257
## Lag 3e+05 0.001463528 -0.0202440828 0.017539643
## Lag 4e+05 0.008396229 -0.0160918846 -0.016798487
## Lag 5e+05 0.003859869 -0.0208920113 -0.024695040
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01063046 0.029248963 -0.009934672
## Lag 2e+05 -0.01162834 0.008072418 -0.018618860
## Lag 3e+05 0.01196546 0.037372123 0.002967483
## Lag 4e+05 0.01578000 -0.030478822 0.001248421
## Lag 5e+05 0.01399681 -0.012613542 0.001928380
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.012470353 0.003658270 -0.0277476196
## Lag 2e+05 -0.008046442 0.009763684 -0.0008693885
## Lag 3e+05 -0.022457371 -0.005274708 -0.0299199312
## Lag 4e+05 -0.018725001 0.008761441 0.0114885925
## Lag 5e+05 0.005299452 -0.009391127 0.0197180266
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.012070816 -0.01414538 0.024201125
## Lag 2e+05 -0.018249011 0.01110926 -0.023028787
## Lag 3e+05 0.017811469 -0.02073407 -0.010077203
## Lag 4e+05 0.014853740 0.01365676 0.011524809
## Lag 5e+05 -0.009943323 -0.01517775 0.003422765
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.4498 0.2695 1.0898
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1977 -0.2121 -1.1982
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6528754 0.7875718 0.2758045
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8433102 0.8320154 0.2308482
## Joint P-value (lower = worse): 0.7042222 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.84024 -1.12084 -0.60216
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.07758 0.02077 -0.37963
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4007743 0.2623556 0.5470695
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9381661 0.9834285 0.7042179
## Joint P-value (lower = worse): 0.8389077 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4961 -0.6390 0.2576
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1190 -0.5697 0.5317
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6198235 0.5228062 0.7967199
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9052781 0.5689107 0.5949412
## Joint P-value (lower = worse): 0.9484161 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.6077 1.5640 0.4276
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.5840 1.7052 1.5777
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.10789969 0.11782802 0.66894872
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.55925236 0.08815563 0.11462551
## Joint P-value (lower = worse): 0.1424581 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.7565 -0.1949 1.8757
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.0085 -0.4302 1.1165
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.07900789 0.84547797 0.06069433
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.31320179 0.66703828 0.26418861
## Joint P-value (lower = worse): 0.1941955 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.2579 0.7303 -1.0786
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.2551 -0.7524 -0.1256
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7965156 0.4651883 0.2807608
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7986409 0.4518302 0.9000303
## Joint P-value (lower = worse): 0.9569326 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4566 -0.4246 -0.7287
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.1565 -0.5067 1.4818
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6479364 0.6711539 0.4662087
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8756597 0.6123817 0.1384026
## Joint P-value (lower = worse): 0.5309442 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.55076 -0.05389 -0.29861
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.18073 -1.14701 0.59100
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5817984 0.9570241 0.7652376
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8565803 0.2513762 0.5545174
## Joint P-value (lower = worse): 0.8805751 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.0003667 40.088 0.23145 0.23263
## nodefactor.deg.main.1 -0.0795333 45.078 0.26026 0.25871
## nodefactor.race..wa.B 0.6763000 16.041 0.09262 0.09394
## nodefactor.race..wa.H -0.4632000 23.583 0.13615 0.13844
## nodematch.race..wa.B 0.0135177 2.880 0.01663 0.01671
## nodematch.race..wa.H -0.1525637 6.927 0.03999 0.03975
## nodematch.race..wa.O -0.4243794 32.677 0.18866 0.18700
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -78.50 -26.500 -0.5000 26.500 79.50
## nodefactor.deg.main.1 -89.00 -30.000 0.0000 30.000 89.00
## nodefactor.race..wa.B -30.52 -10.517 0.4832 11.483 32.48
## nodefactor.race..wa.H -46.34 -16.340 -0.3400 15.660 46.66
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.1815 4.819 13.82
## nodematch.race..wa.O -64.08 -23.081 -1.0808 21.919 63.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75567574
## nodefactor.deg.main.1 0.75567574 1.00000000
## nodefactor.race..wa.B 0.34878341 0.23595585
## nodefactor.race..wa.H 0.46408333 0.39925610
## nodematch.race..wa.B 0.05407078 0.02733345
## nodematch.race..wa.H 0.12069718 0.11931936
## nodematch.race..wa.O 0.78474192 0.57816856
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.348783411 0.464083328
## nodefactor.deg.main.1 0.235955847 0.399256102
## nodefactor.race..wa.B 1.000000000 0.106971114
## nodefactor.race..wa.H 0.106971114 1.000000000
## nodematch.race..wa.B 0.300638176 -0.001563697
## nodematch.race..wa.H -0.008770457 0.498985173
## nodematch.race..wa.O -0.018362660 -0.037391781
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.054070777 0.120697180
## nodefactor.deg.main.1 0.027333453 0.119319361
## nodefactor.race..wa.B 0.300638176 -0.008770457
## nodefactor.race..wa.H -0.001563697 0.498985173
## nodematch.race..wa.B 1.000000000 0.007093001
## nodematch.race..wa.H 0.007093001 1.000000000
## nodematch.race..wa.O 0.004282978 -0.001179403
## nodematch.race..wa.O
## edges 0.784741922
## nodefactor.deg.main.1 0.578168564
## nodefactor.race..wa.B -0.018362660
## nodefactor.race..wa.H -0.037391781
## nodematch.race..wa.B 0.004282978
## nodematch.race..wa.H -0.001179403
## nodematch.race..wa.O 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.020346379 0.007257429 0.0023991539
## Lag 2e+05 0.008336525 0.008624913 0.0264586129
## Lag 3e+05 -0.008185406 -0.034032252 -0.0005977681
## Lag 4e+05 -0.016419040 -0.032652433 0.0276314928
## Lag 5e+05 -0.007842153 -0.002968748 0.0045604650
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.005322937 0.003121216 -0.021671753
## Lag 2e+05 0.007151695 0.014134361 -0.010609441
## Lag 3e+05 -0.024763491 -0.007253842 -0.003569679
## Lag 4e+05 -0.007066556 -0.011904789 0.017600048
## Lag 5e+05 0.039670993 0.021367959 0.031751262
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0092642715
## Lag 2e+05 -0.0008065677
## Lag 3e+05 0.0016950608
## Lag 4e+05 -0.0248122777
## Lag 5e+05 -0.0256992975
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.033432452 -0.021140204 -0.001643601
## Lag 2e+05 -0.009539155 -0.002464447 0.011168133
## Lag 3e+05 0.039421042 0.025503390 -0.016759099
## Lag 4e+05 -0.005227752 -0.003702030 0.004159357
## Lag 5e+05 -0.013595720 0.015074988 -0.002094999
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006490441 -0.010859749 0.014018834
## Lag 2e+05 0.005612046 -0.010218864 -0.041821296
## Lag 3e+05 0.014641463 -0.038114557 0.002095162
## Lag 4e+05 -0.017766035 0.027919064 -0.005688775
## Lag 5e+05 -0.019271565 -0.005792261 -0.017025754
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 -0.0118583201
## Lag 2e+05 -0.0321352592
## Lag 3e+05 0.0181139106
## Lag 4e+05 -0.0269303725
## Lag 5e+05 -0.0001396649
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.008358189 -0.019641560 0.002205620
## Lag 2e+05 -0.009429479 -0.015899415 -0.019319928
## Lag 3e+05 0.020139587 0.015697208 0.005260236
## Lag 4e+05 -0.003190494 0.003094338 0.011448050
## Lag 5e+05 0.003302542 0.030350441 0.003683434
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.022040551 0.016087606 -0.009162634
## Lag 2e+05 -0.017510889 -0.013538269 0.002901417
## Lag 3e+05 -0.004109120 0.041005952 -0.005402268
## Lag 4e+05 -0.023275506 0.007468984 -0.005360512
## Lag 5e+05 -0.004071408 0.002865810 -0.018305342
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.005905203
## Lag 2e+05 0.006827892
## Lag 3e+05 0.026442015
## Lag 4e+05 0.006701167
## Lag 5e+05 -0.009486472
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.009659987 0.01079896 -0.005904286
## Lag 2e+05 0.004266297 -0.01140462 0.043051041
## Lag 3e+05 0.013721497 0.01611263 -0.007483191
## Lag 4e+05 -0.001237171 0.01227068 0.032858755
## Lag 5e+05 -0.001599683 0.01825667 0.020195799
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0065691335 0.011446100 0.010205913
## Lag 2e+05 0.0032697396 -0.025824317 -0.034037605
## Lag 3e+05 -0.0007877063 0.005548393 0.007604281
## Lag 4e+05 -0.0083929417 0.019109810 0.006101996
## Lag 5e+05 -0.0042518365 0.027201148 -0.009351544
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.004368116
## Lag 2e+05 0.015074423
## Lag 3e+05 0.011569052
## Lag 4e+05 -0.004801247
## Lag 5e+05 0.008002505
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0128824505 0.004968885 0.020246188
## Lag 2e+05 -0.0008600489 0.006758939 -0.003393436
## Lag 3e+05 -0.0038571760 0.005820086 0.011088105
## Lag 4e+05 -0.0096543200 -0.004416776 -0.001946214
## Lag 5e+05 0.0037368822 -0.003221812 0.011761987
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.004322357 0.01505552 -0.023040228
## Lag 2e+05 -0.007859073 -0.01688641 0.005403930
## Lag 3e+05 -0.008792907 -0.01648602 -0.010315436
## Lag 4e+05 0.003951401 -0.01513773 -0.005091850
## Lag 5e+05 0.011894261 -0.02705870 0.003799587
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.024408207
## Lag 2e+05 -0.005702051
## Lag 3e+05 -0.015572724
## Lag 4e+05 -0.013748425
## Lag 5e+05 0.002397250
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.023450038 -0.020089147 0.0116543781
## Lag 2e+05 -0.001952285 -0.011631077 0.0058145025
## Lag 3e+05 0.037828921 0.011849570 -0.0008881523
## Lag 4e+05 0.000463904 0.007704660 0.0088855258
## Lag 5e+05 0.015062892 -0.004875415 0.0109068840
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.004107895 0.0002986658 0.004131773
## Lag 2e+05 0.007878321 0.0520089165 0.006513362
## Lag 3e+05 -0.012233228 0.0083847460 -0.041820513
## Lag 4e+05 0.011467523 -0.0036065884 -0.005123006
## Lag 5e+05 0.023830435 0.0020294657 0.030109413
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.020415890
## Lag 2e+05 0.002197100
## Lag 3e+05 0.024719054
## Lag 4e+05 -0.001904416
## Lag 5e+05 0.008128940
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 -0.002390172 6.969027e-03 -0.013287328
## Lag 2e+05 -0.016189638 -1.287775e-05 0.012327880
## Lag 3e+05 0.006999612 1.264744e-03 -0.013569386
## Lag 4e+05 0.001622088 -1.406119e-02 -0.030649081
## Lag 5e+05 -0.010399368 -1.313095e-02 0.006087377
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.014046068 0.009281620 0.02900322
## Lag 2e+05 -0.007225317 0.029520009 -0.01475909
## Lag 3e+05 -0.031780039 0.024476791 -0.02320434
## Lag 4e+05 0.013664691 -0.002804322 -0.01425053
## Lag 5e+05 -0.008628453 -0.018140815 -0.02639694
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.003875352
## Lag 2e+05 -0.034757764
## Lag 3e+05 0.004239766
## Lag 4e+05 0.017164535
## Lag 5e+05 -0.016174285
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.03194833 0.008662342 0.05169358
## Lag 2e+05 0.01642757 0.005724186 -0.02332861
## Lag 3e+05 -0.01188635 -0.018220038 0.01870458
## Lag 4e+05 0.01574487 0.033037564 -0.01017263
## Lag 5e+05 0.01596508 0.006968348 0.03322637
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.002491915 -0.005158260 0.0157177324
## Lag 2e+05 0.004433865 -0.020937794 0.0005948879
## Lag 3e+05 0.009709178 0.013374941 -0.0004356022
## Lag 4e+05 -0.002578136 0.009232686 0.0159932375
## Lag 5e+05 0.021716248 0.019420266 0.0064974817
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0006988953
## Lag 2e+05 0.0266633979
## Lag 3e+05 -0.0192785315
## Lag 4e+05 0.0181656266
## Lag 5e+05 -0.0147040867
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.32937 1.17666 0.91220
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.04825 0.56778 0.90087
## nodematch.race..wa.O
## 0.17095
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7418739 0.2393302 0.3616659
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.9615144 0.5701828 0.3676576
## nodematch.race..wa.O
## 0.8642615
## Joint P-value (lower = worse): 0.860803 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.61479 0.53606 0.33620
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -1.12447 -0.33337 -0.03493
## nodematch.race..wa.O
## 1.37407
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5386930 0.5919155 0.7367237
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.2608124 0.7388525 0.9721342
## nodematch.race..wa.O
## 0.1694192
## Joint P-value (lower = worse): 0.7933805 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3535 1.3636 -1.9832
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -1.2037 -2.1218 0.4305
## nodematch.race..wa.O
## 1.3037
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.72372673 0.17268046 0.04734771
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.22870741 0.03385666 0.66685409
## nodematch.race..wa.O
## 0.19231989
## Joint P-value (lower = worse): 0.03873953 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3656 -0.8904 0.6018
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.2599 1.4363 0.6860
## nodematch.race..wa.O
## -0.5614
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7146980 0.3732331 0.5473029
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.7949034 0.1509087 0.4927078
## nodematch.race..wa.O
## 0.5745022
## Joint P-value (lower = worse): 0.5760181 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.2076 -1.2937 0.2086
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 1.3264 -0.4162 0.6691
## nodematch.race..wa.O
## -0.8358
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8355108 0.1957532 0.8347363
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.1847174 0.6772757 0.5034156
## nodematch.race..wa.O
## 0.4032946
## Joint P-value (lower = worse): 0.5798393 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9140 0.3557 0.8174
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 1.1499 -0.3872 1.0626
## nodematch.race..wa.O
## 0.0714
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3606940 0.7220846 0.4137030
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.2501857 0.6985740 0.2879734
## nodematch.race..wa.O
## 0.9430815
## Joint P-value (lower = worse): 0.8740967 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.4195 0.1408 -0.5261
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.3835 0.4382 1.8054
## nodematch.race..wa.O
## -0.3776
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.67484698 0.88802261 0.59880903
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.70132077 0.66125704 0.07101767
## nodematch.race..wa.O
## 0.70573529
## Joint P-value (lower = worse): 0.6427369 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.9586 -0.1157 -1.4526
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.2728 2.0765 0.1456
## nodematch.race..wa.O
## -0.1517
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.33774196 0.90791266 0.14632866
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.78503908 0.03784736 0.88423640
## nodematch.race..wa.O
## 0.87943263
## Joint P-value (lower = worse): 0.1592844 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.47593 40.071 0.23135 0.23067
## nodefactor.deg.main.1 -0.78657 45.209 0.26102 0.26141
## nodefactor.race..wa.B -0.32920 15.925 0.09194 0.09195
## nodefactor.race..wa.H -1.50490 23.465 0.13548 0.13432
## nodefactor.region.EW -0.40953 18.781 0.10843 0.10754
## nodefactor.region.OW 0.88057 36.695 0.21186 0.21317
## nodematch.race..wa.B -0.04772 2.880 0.01663 0.01659
## nodematch.race..wa.H -0.51176 6.864 0.03963 0.03921
## nodematch.race..wa.O 1.68085 32.657 0.18854 0.18792
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -77.50 -26.500 0.5000 27.500 78.50
## nodefactor.deg.main.1 -89.00 -31.000 -1.0000 29.000 89.00
## nodefactor.race..wa.B -31.52 -11.517 -0.5168 10.483 30.48
## nodefactor.race..wa.H -47.34 -17.340 -1.3400 13.660 45.66
## nodefactor.region.EW -36.59 -13.588 -0.5885 12.412 36.41
## nodefactor.region.OW -71.25 -24.255 0.7450 25.745 73.75
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 5.52
## nodematch.race..wa.H -13.18 -5.181 -1.1815 3.819 13.82
## nodematch.race..wa.O -62.08 -20.081 1.9192 23.919 65.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75666469
## nodefactor.deg.main.1 0.75666469 1.00000000
## nodefactor.race..wa.B 0.34538833 0.23435368
## nodefactor.race..wa.H 0.46236378 0.39612780
## nodefactor.region.EW 0.38877954 0.30171174
## nodefactor.region.OW 0.66076311 0.45482557
## nodematch.race..wa.B 0.05426938 0.03365535
## nodematch.race..wa.H 0.11580470 0.11210820
## nodematch.race..wa.O 0.78819998 0.58094356
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.34538833 0.46236378
## nodefactor.deg.main.1 0.23435368 0.39612780
## nodefactor.race..wa.B 1.00000000 0.09885660
## nodefactor.race..wa.H 0.09885660 1.00000000
## nodefactor.region.EW 0.08981442 0.28433482
## nodefactor.region.OW 0.20818208 0.29471141
## nodematch.race..wa.B 0.30898892 -0.01268684
## nodematch.race..wa.H -0.02744403 0.49717804
## nodematch.race..wa.O -0.01560240 -0.03391010
## nodefactor.region.EW nodefactor.region.OW
## edges 0.388779539 0.66076311
## nodefactor.deg.main.1 0.301711740 0.45482557
## nodefactor.race..wa.B 0.089814422 0.20818208
## nodefactor.race..wa.H 0.284334822 0.29471141
## nodefactor.region.EW 1.000000000 0.11313942
## nodefactor.region.OW 0.113139422 1.00000000
## nodematch.race..wa.B 0.001161155 0.03191665
## nodematch.race..wa.H 0.104108353 0.07681344
## nodematch.race..wa.O 0.265857231 0.53468551
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.054269383 0.1158046992
## nodefactor.deg.main.1 0.033655353 0.1121082022
## nodefactor.race..wa.B 0.308988918 -0.0274440287
## nodefactor.race..wa.H -0.012686836 0.4971780368
## nodefactor.region.EW 0.001161155 0.1041083533
## nodefactor.region.OW 0.031916654 0.0768134422
## nodematch.race..wa.B 1.000000000 -0.0096487696
## nodematch.race..wa.H -0.009648770 1.0000000000
## nodematch.race..wa.O 0.004910681 0.0006486809
## nodematch.race..wa.O
## edges 0.7881999818
## nodefactor.deg.main.1 0.5809435560
## nodefactor.race..wa.B -0.0156024023
## nodefactor.race..wa.H -0.0339101030
## nodefactor.region.EW 0.2658572311
## nodefactor.region.OW 0.5346855062
## nodematch.race..wa.B 0.0049106809
## nodematch.race..wa.H 0.0006486809
## nodematch.race..wa.O 1.0000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.015306435 -0.018052938 0.00132814
## Lag 2e+05 -0.002918860 0.025922884 -0.01506075
## Lag 3e+05 0.004425414 -0.005903431 0.01340845
## Lag 4e+05 0.029138614 0.010364324 0.01215767
## Lag 5e+05 -0.025651556 -0.009713273 -0.02455062
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.022348759 -0.012341794 0.005262533
## Lag 2e+05 -0.023673776 -0.030555899 0.005043605
## Lag 3e+05 0.006939928 -0.004122538 0.005170901
## Lag 4e+05 0.004714846 -0.009094313 0.012680795
## Lag 5e+05 0.005556443 -0.019156415 -0.013788248
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.017734606 0.005118715 0.020610373
## Lag 2e+05 0.006912085 0.002286716 -0.006841280
## Lag 3e+05 0.022474364 -0.031339122 0.001099243
## Lag 4e+05 -0.011148104 0.008197210 0.032459904
## Lag 5e+05 -0.012516422 0.007180503 -0.010381251
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0131101818 0.018947703 0.020371391
## Lag 2e+05 -0.0017693739 -0.008455363 0.002862859
## Lag 3e+05 0.0021289481 -0.006500097 -0.018891447
## Lag 4e+05 0.0007318604 -0.012799490 0.004167525
## Lag 5e+05 -0.0127590483 -0.036307119 -0.009775620
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008126948 0.015852547 0.002965173
## Lag 2e+05 0.003764299 0.014686695 -0.012398148
## Lag 3e+05 -0.031758061 -0.008976189 -0.030125373
## Lag 4e+05 -0.003743919 0.005114454 0.008317561
## Lag 5e+05 -0.005915130 0.005938460 -0.010309619
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.0000000000
## Lag 1e+05 0.01476034 -0.023544847 -0.0127970573
## Lag 2e+05 -0.01344060 -0.020653545 0.0149560035
## Lag 3e+05 0.01122950 -0.008804326 -0.0005471229
## Lag 4e+05 0.02721754 -0.017387914 -0.0019084030
## Lag 5e+05 -0.03439105 0.004839526 -0.0024251507
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.010447328 -0.0022922647 0.016690741
## Lag 2e+05 0.002562907 0.0167040053 0.007914421
## Lag 3e+05 0.009542835 0.0108454200 0.010639006
## Lag 4e+05 -0.020837504 -0.0088594085 0.003679102
## Lag 5e+05 0.008045893 -0.0001280241 0.005148860
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.002825576 0.0182685898 -0.006363499
## Lag 2e+05 0.005749679 0.0050833991 0.013130682
## Lag 3e+05 -0.003501010 -0.0101991373 -0.013225344
## Lag 4e+05 0.015505487 0.0002853342 -0.016784753
## Lag 5e+05 0.026992074 -0.0044628958 -0.003044371
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 -0.001282273 4.758006e-03 0.005160048
## Lag 2e+05 0.005670709 -1.754309e-02 0.027880685
## Lag 3e+05 -0.030596267 -2.439940e-03 0.012349108
## Lag 4e+05 -0.010581956 2.735818e-02 -0.012393788
## Lag 5e+05 0.004535701 -4.229668e-05 -0.001762647
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.025260542 -0.026998767 -0.0203873053
## Lag 2e+05 -0.005809487 0.029807133 -0.0002485406
## Lag 3e+05 -0.026344268 0.008551758 0.0277406828
## Lag 4e+05 0.004155856 0.015435780 0.0120652261
## Lag 5e+05 -0.005781379 -0.036251018 0.0015232837
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0159506737 0.004718468 -0.008474810
## Lag 2e+05 0.0037765356 0.008062613 -0.006520565
## Lag 3e+05 -0.0019222818 0.003361787 -0.008091097
## Lag 4e+05 -0.0007515139 0.007874367 -0.005389109
## Lag 5e+05 0.0075045902 -0.006946675 -0.007486147
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 0.0263192993 -0.0309300565 -0.027798249
## Lag 2e+05 0.0125463265 0.0003658643 -0.013285681
## Lag 3e+05 -0.0247372411 0.0212922262 -0.010045939
## Lag 4e+05 0.0002955329 0.0078951133 0.006639135
## Lag 5e+05 0.0046948821 -0.0087321970 -0.006410832
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0007767478 -0.033445718 -0.0112920446
## Lag 2e+05 0.0126253264 0.012730531 -0.0076570745
## Lag 3e+05 -0.0024801973 0.008120142 0.0179439855
## Lag 4e+05 0.0078466505 0.011846235 0.0001473282
## Lag 5e+05 0.0006099981 0.006420157 0.0125179207
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.014625641 -0.014184193 0.010341751
## Lag 2e+05 0.011924162 0.011923194 -0.015350243
## Lag 3e+05 -0.006849913 -0.036408412 -0.007563223
## Lag 4e+05 -0.004274138 0.011827097 0.010560889
## Lag 5e+05 0.006557485 -0.005417356 0.032991211
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0048137947 -0.0004479711 -0.005999612
## Lag 2e+05 0.0133257104 -0.0209609187 0.008132494
## Lag 3e+05 -0.0099064445 -0.0476163534 -0.010294433
## Lag 4e+05 -0.0090396159 -0.0014205766 -0.001789669
## Lag 5e+05 0.0006072016 -0.0277650004 0.008619264
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.009391393 0.011945380 0.002310709
## Lag 2e+05 0.001398434 -0.010611825 0.017953613
## Lag 3e+05 -0.007346506 0.003391769 -0.008381760
## Lag 4e+05 -0.012126942 -0.021727262 0.008481824
## Lag 5e+05 -0.009122910 -0.001547922 -0.004626226
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0139031380 0.0049132295 -0.020281250
## Lag 2e+05 0.0007152583 0.0105711430 -0.001647741
## Lag 3e+05 0.0062528211 0.0180638879 -0.004061461
## Lag 4e+05 -0.0155042732 -0.0067776650 0.002843936
## Lag 5e+05 -0.0110546559 -0.0004468045 0.007485176
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.023598350 -0.0104074430 0.0127384143
## Lag 2e+05 -0.005480389 0.0031745426 0.0009307375
## Lag 3e+05 -0.004101450 0.0039285611 0.0001273943
## Lag 4e+05 -0.005725877 0.0001523318 -0.0031507134
## Lag 5e+05 -0.037219023 -0.0009030414 -0.0098082537
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.011594643 0.0002826664 -0.013071480
## Lag 2e+05 0.012237969 -0.0178709801 -0.002065485
## Lag 3e+05 0.003610117 0.0005950716 0.018719633
## Lag 4e+05 0.013104222 0.0030829340 -0.006131741
## Lag 5e+05 -0.017952335 -0.0220786928 0.001486529
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.025017860 0.0044155118 -0.004666238
## Lag 2e+05 0.005843071 0.0008068439 0.022664306
## Lag 3e+05 -0.020877121 -0.0143439536 -0.004133951
## Lag 4e+05 0.021106151 0.0190497400 0.001038634
## Lag 5e+05 -0.019865626 -0.0028894109 -0.011822922
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.003827034 -0.007935242 0.008313818
## Lag 2e+05 -0.020531069 -0.006467763 0.010320889
## Lag 3e+05 0.003889147 0.006805101 0.001265917
## Lag 4e+05 -0.001976969 -0.006885797 -0.001340558
## Lag 5e+05 -0.007986954 -0.006658264 -0.010960895
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010399245 -0.006682664 0.009256780
## Lag 2e+05 0.002897909 0.018185848 0.026399108
## Lag 3e+05 -0.020300118 0.004607525 0.002754848
## Lag 4e+05 -0.014557238 -0.007906497 0.018838609
## Lag 5e+05 0.008274621 -0.002117132 0.023194382
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.002216389 0.0004650981 0.0101808509
## Lag 2e+05 0.010330800 -0.0360264596 0.0374094109
## Lag 3e+05 -0.028218980 0.0466678545 0.0017403295
## Lag 4e+05 0.013598505 0.0101534953 -0.0007818516
## Lag 5e+05 -0.012772702 -0.0032311997 0.0074307124
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018430728 0.027086471 -0.003957709
## Lag 2e+05 0.015118497 0.009490426 -0.010949490
## Lag 3e+05 0.011214195 0.013294020 -0.022170848
## Lag 4e+05 -0.006568312 -0.011633989 -0.030351381
## Lag 5e+05 -0.021679373 0.007529639 0.005215417
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.6959 0.1829 0.4877
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.7269 0.1075 -2.5487
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.6848 -1.0162 -2.1654
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08989815 0.85484227 0.62577610
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.46726614 0.91442325 0.01081184
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.49347777 0.30955724 0.03035550
## Joint P-value (lower = worse): 0.07582541 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.007836 -0.380024 -0.238627
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.108207 1.155100 0.057517
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.074759 1.167007 0.517084
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9937481 0.7039280 0.8113950
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9138312 0.2480494 0.9541333
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9404061 0.2432076 0.6050976
## Joint P-value (lower = worse): 0.8434678 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3436 -0.8177 -0.8565
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3646 0.4779 0.5406
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7898 1.0666 0.7338
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7311181 0.4135347 0.3917005
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7153757 0.6327363 0.5887490
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4296624 0.2861633 0.4630463
## Joint P-value (lower = worse): 0.730802 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.71705 -1.00242 1.11639
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.90682 -0.04609 -0.67051
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.36265 -1.43651 -1.60100
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08597032 0.31614142 0.26425710
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.36450386 0.96324135 0.50252986
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.17299378 0.15085836 0.10937725
## Joint P-value (lower = worse): 0.3650856 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.44027 1.55756 -0.20203
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.29757 -2.12386 -0.40313
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.08772 -0.15291 0.55270
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.65973912 0.11933876 0.83989078
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.19443475 0.03368217 0.68685593
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.93009707 0.87846572 0.58047174
## Joint P-value (lower = worse): 0.1470618 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3070 -0.5344 1.1282
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.8037 -1.7452 1.0294
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.3221 0.6083 -0.4681
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.75880940 0.59307742 0.25923623
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.42159043 0.08095277 0.30328192
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.18613691 0.54296043 0.63969356
## Joint P-value (lower = worse): 0.184885 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3606 1.1080 -0.5898
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.0073 -0.9405 0.7627
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.5817 -2.7745 0.4128
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.718405510 0.267876090 0.555349389
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.313812353 0.346954918 0.445664910
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.560740977 0.005528252 0.679788317
## Joint P-value (lower = worse): 0.02274662 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.8159 0.2994 -0.4321
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9135 -0.4469 -0.5666
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.2939 -0.3949 -1.1324
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4145300 0.7646370 0.6656552
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3610058 0.6549203 0.5709743
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1957132 0.6929414 0.2574465
## Joint P-value (lower = worse): 0.5995688 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 3.036133 40.139 0.23174 0.22932
## nodefactor.deg.main.1 3.000567 45.354 0.26185 0.26009
## nodefactor.race..wa.B -0.087833 15.960 0.09214 0.09279
## nodefactor.race..wa.H 1.645867 23.638 0.13647 0.13636
## nodefactor.region.EW -0.012600 18.897 0.10910 0.10870
## nodefactor.region.OW 2.469367 36.366 0.20996 0.20826
## nodematch.race..wa.B 0.005751 2.890 0.01669 0.01668
## nodematch.race..wa.H 0.061336 6.949 0.04012 0.04060
## nodematch.race..wa.O 1.576854 32.785 0.18929 0.18827
## absdiff.sqrt.age 5.325223 45.279 0.26142 0.25908
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -75.50 -23.500 3.5000 29.500 81.50
## nodefactor.deg.main.1 -86.00 -28.000 3.0000 34.000 92.00
## nodefactor.race..wa.B -30.52 -10.517 -0.5168 10.483 31.48
## nodefactor.race..wa.H -44.34 -14.340 1.6600 17.660 47.66
## nodefactor.region.EW -36.59 -12.588 -0.5885 12.412 37.41
## nodefactor.region.OW -68.25 -22.255 2.7450 26.745 73.75
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 5.52
## nodematch.race..wa.H -13.18 -5.181 -0.1815 4.819 13.82
## nodematch.race..wa.O -62.08 -21.081 0.9192 23.919 64.92
## absdiff.sqrt.age -82.95 -25.513 4.9456 35.828 95.00
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75679144
## nodefactor.deg.main.1 0.75679144 1.00000000
## nodefactor.race..wa.B 0.34088449 0.23315514
## nodefactor.race..wa.H 0.46682144 0.39489733
## nodefactor.region.EW 0.39132611 0.29605980
## nodefactor.region.OW 0.65928284 0.45763357
## nodematch.race..wa.B 0.05914573 0.04279328
## nodematch.race..wa.H 0.12844616 0.12155482
## nodematch.race..wa.O 0.78498271 0.58210417
## absdiff.sqrt.age 0.73425035 0.55529280
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.34088449 0.466821441
## nodefactor.deg.main.1 0.23315514 0.394897328
## nodefactor.race..wa.B 1.00000000 0.107193923
## nodefactor.race..wa.H 0.10719392 1.000000000
## nodefactor.region.EW 0.07791901 0.298919920
## nodefactor.region.OW 0.20214304 0.287051440
## nodematch.race..wa.B 0.31316823 -0.005901527
## nodematch.race..wa.H -0.01061830 0.502863426
## nodematch.race..wa.O -0.02617551 -0.034735421
## absdiff.sqrt.age 0.25162652 0.350603871
## nodefactor.region.EW nodefactor.region.OW
## edges 0.391326114 0.65928284
## nodefactor.deg.main.1 0.296059799 0.45763357
## nodefactor.race..wa.B 0.077919007 0.20214304
## nodefactor.race..wa.H 0.298919920 0.28705144
## nodefactor.region.EW 1.000000000 0.10990237
## nodefactor.region.OW 0.109902374 1.00000000
## nodematch.race..wa.B 0.005117035 0.03089164
## nodematch.race..wa.H 0.122424431 0.07154405
## nodematch.race..wa.O 0.265983367 0.53599269
## absdiff.sqrt.age 0.282356620 0.48660288
## nodematch.race..wa.B nodematch.race..wa.H
## edges 5.914573e-02 1.284462e-01
## nodefactor.deg.main.1 4.279328e-02 1.215548e-01
## nodefactor.race..wa.B 3.131682e-01 -1.061830e-02
## nodefactor.race..wa.H -5.901527e-03 5.028634e-01
## nodefactor.region.EW 5.117035e-03 1.224244e-01
## nodefactor.region.OW 3.089164e-02 7.154405e-02
## nodematch.race..wa.B 1.000000e+00 -1.094911e-05
## nodematch.race..wa.H -1.094911e-05 1.000000e+00
## nodematch.race..wa.O 4.653035e-03 5.624247e-03
## absdiff.sqrt.age 4.487385e-02 9.655965e-02
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.784982707 0.73425035
## nodefactor.deg.main.1 0.582104171 0.55529280
## nodefactor.race..wa.B -0.026175510 0.25162652
## nodefactor.race..wa.H -0.034735421 0.35060387
## nodefactor.region.EW 0.265983367 0.28235662
## nodefactor.region.OW 0.535992694 0.48660288
## nodematch.race..wa.B 0.004653035 0.04487385
## nodematch.race..wa.H 0.005624247 0.09655965
## nodematch.race..wa.O 1.000000000 0.57135652
## absdiff.sqrt.age 0.571356515 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.003525380 -0.005947055 -0.0138529089
## Lag 2e+05 0.002650839 -0.006208131 0.0067446142
## Lag 3e+05 0.006482064 -0.012386904 -0.0236135291
## Lag 4e+05 -0.025657403 -0.027468777 -0.0001782027
## Lag 5e+05 0.003464240 0.008215969 -0.0132382485
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.01222040 0.0098701402 0.008803227
## Lag 2e+05 -0.02196102 0.0233150288 0.033912992
## Lag 3e+05 -0.01055248 0.0009157982 -0.002385281
## Lag 4e+05 -0.01108913 0.0029040056 -0.002851153
## Lag 5e+05 -0.02365266 -0.0230257971 0.019007198
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.011201420 0.038493233 0.008397704
## Lag 2e+05 0.001090390 -0.003734910 0.016578998
## Lag 3e+05 -0.002957922 0.001188365 0.027616627
## Lag 4e+05 -0.035883724 -0.013582472 0.005207448
## Lag 5e+05 0.023136778 -0.013789916 -0.006364059
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 -0.0116829827
## Lag 2e+05 -0.0094194502
## Lag 3e+05 -0.0009110845
## Lag 4e+05 -0.0404870523
## Lag 5e+05 -0.0007622560
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.031919346 -0.015841994 -0.01149043
## Lag 2e+05 -0.003166948 -0.025931591 -0.01908299
## Lag 3e+05 0.031966234 0.032277641 -0.01106794
## Lag 4e+05 0.005558704 -0.010517414 -0.01146389
## Lag 5e+05 -0.018594835 -0.004398479 0.02780604
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.048422868 -0.020357805 -0.017600267
## Lag 2e+05 0.022819786 -0.003341520 0.010744515
## Lag 3e+05 0.002651218 -0.007991695 0.009588094
## Lag 4e+05 -0.012307167 -0.003331527 -0.028285092
## Lag 5e+05 0.013094326 -0.021119152 -0.015114698
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.003377120 0.009366792 -0.007388680
## Lag 2e+05 0.018403909 -0.013284202 -0.006054701
## Lag 3e+05 -0.005355753 -0.017973034 0.020217681
## Lag 4e+05 0.027839266 -0.005827875 -0.001293279
## Lag 5e+05 -0.005872895 0.035720949 -0.020722114
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.021033201
## Lag 2e+05 -0.012039030
## Lag 3e+05 0.008318685
## Lag 4e+05 0.021597542
## Lag 5e+05 -0.009519221
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.012136667 0.009915924 -0.011598561
## Lag 2e+05 -0.044755115 -0.031677419 -0.022369786
## Lag 3e+05 0.001841987 0.015403488 -0.001669568
## Lag 4e+05 -0.006624650 -0.017690808 -0.029496836
## Lag 5e+05 -0.018458553 -0.010738995 -0.008244597
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 0.006710914 -6.633948e-03 -0.036688612
## Lag 2e+05 -0.023287517 -7.356797e-03 -0.024298586
## Lag 3e+05 -0.004828836 -6.775267e-05 -0.024445962
## Lag 4e+05 0.012323691 1.332838e-02 0.006662862
## Lag 5e+05 -0.018789115 2.043752e-02 -0.026441648
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01466441 0.032863503 -0.016471913
## Lag 2e+05 -0.01604466 0.009461590 -0.030383400
## Lag 3e+05 -0.02185478 0.014120354 -0.008890067
## Lag 4e+05 -0.01955723 0.013455177 0.006159624
## Lag 5e+05 -0.01108289 -0.004100987 0.004704283
## absdiff.sqrt.age
## Lag 0 1.00000000
## Lag 1e+05 -0.03154152
## Lag 2e+05 -0.04540585
## Lag 3e+05 0.00102928
## Lag 4e+05 -0.02230002
## Lag 5e+05 -0.01111823
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.012912349 0.0084845483 -0.0103706780
## Lag 2e+05 -0.001994479 -0.0038838674 0.0156029241
## Lag 3e+05 -0.001324666 0.0049230681 0.0155268284
## Lag 4e+05 -0.022066024 -0.0267043042 0.0215430485
## Lag 5e+05 0.010666914 -0.0008148622 0.0004980592
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.007867186 -0.008773321 -0.012665969
## Lag 2e+05 0.004882148 -0.008836749 -0.015706595
## Lag 3e+05 0.008026628 0.007753655 -0.012247478
## Lag 4e+05 -0.020299065 -0.013087254 0.003170685
## Lag 5e+05 -0.013328686 -0.019227111 0.008586260
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.002901487 0.013958607 0.002103240
## Lag 2e+05 -0.019354702 0.015219702 -0.008607253
## Lag 3e+05 -0.003436107 -0.010506831 0.003252033
## Lag 4e+05 0.015225248 -0.016147658 -0.005759139
## Lag 5e+05 -0.008337202 0.002882331 0.018208769
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.001606929
## Lag 2e+05 0.004638391
## Lag 3e+05 0.014221249
## Lag 4e+05 0.004492374
## Lag 5e+05 -0.002668298
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.013357999 -0.024688436 0.013450679
## Lag 2e+05 -0.013439290 -0.003946531 0.002529023
## Lag 3e+05 -0.011429223 -0.008271233 -0.015790217
## Lag 4e+05 0.019035307 0.010506640 0.010941237
## Lag 5e+05 0.001421248 0.004295797 -0.008716897
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.022234893 -0.009647018 -0.027540949
## Lag 2e+05 -0.014895510 -0.025062401 -0.014680512
## Lag 3e+05 -0.007155976 -0.019444813 -0.006754539
## Lag 4e+05 -0.008482358 0.013127562 -0.015949691
## Lag 5e+05 0.026320673 -0.003592270 -0.009814384
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.013309315 0.011555677 -0.019676039
## Lag 2e+05 0.010165583 -0.015543039 -0.013009029
## Lag 3e+05 -0.007021470 -0.010458431 0.022492434
## Lag 4e+05 -0.037763202 0.001802079 0.025786467
## Lag 5e+05 0.009848782 0.026872257 0.004224709
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.005168424
## Lag 2e+05 -0.027125651
## Lag 3e+05 -0.008583283
## Lag 4e+05 0.033031827
## Lag 5e+05 0.022661333
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008253534 0.015898759 0.032063248
## Lag 2e+05 0.002474595 0.021039335 -0.023615082
## Lag 3e+05 0.023619901 0.024602429 -0.004488667
## Lag 4e+05 -0.026360181 -0.006133302 0.006938368
## Lag 5e+05 0.019182125 0.012223013 0.005978271
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.005115454 -0.030780080 -8.062468e-03
## Lag 2e+05 0.000484256 0.006346438 -3.433132e-05
## Lag 3e+05 0.012767446 0.017141250 2.749321e-02
## Lag 4e+05 -0.006862961 -0.008052323 -2.778476e-02
## Lag 5e+05 0.012875013 -0.006732726 2.243729e-03
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.018704893 0.011914458 0.0039600651
## Lag 2e+05 -0.017131962 0.015882479 0.0115316266
## Lag 3e+05 0.001827136 0.006646805 -0.0006106429
## Lag 4e+05 0.019590327 -0.002387818 -0.0185101675
## Lag 5e+05 0.012888176 0.008613483 0.0248701675
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 0.0007974126
## Lag 2e+05 0.0071893861
## Lag 3e+05 -0.0040030835
## Lag 4e+05 -0.0064511000
## Lag 5e+05 0.0031036223
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0001802699 0.008685442 0.046315981
## Lag 2e+05 -0.0049541976 -0.008099083 -0.004700357
## Lag 3e+05 -0.0033122765 0.002586778 0.010180023
## Lag 4e+05 0.0032572790 0.001953264 0.004077353
## Lag 5e+05 -0.0100894642 0.001063313 -0.009567668
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.049855195 0.0149911755 -0.0005039103
## Lag 2e+05 0.016154316 0.0037906499 -0.0036587992
## Lag 3e+05 0.018223495 -0.0346875328 0.0143919830
## Lag 4e+05 -0.014941266 0.0001791233 0.0220529506
## Lag 5e+05 0.007956114 -0.0259601736 -0.0213489461
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.012889698 0.017631250 -0.011776658
## Lag 2e+05 0.019958925 0.015222414 -0.015334751
## Lag 3e+05 -0.007140378 0.003454250 -0.002745299
## Lag 4e+05 0.004748315 -0.003132181 0.005132262
## Lag 5e+05 -0.018065832 0.001888240 -0.012204515
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.021901412
## Lag 2e+05 -0.016096734
## Lag 3e+05 -0.002502099
## Lag 4e+05 -0.007300720
## Lag 5e+05 0.002563104
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.011742886 -0.011416213 -0.0004473439
## Lag 2e+05 -0.009586745 0.005073916 0.0044085935
## Lag 3e+05 -0.002078646 -0.003268665 -0.0053718574
## Lag 4e+05 0.012356225 0.019547148 0.0107660564
## Lag 5e+05 0.015798013 0.010098175 0.0279004412
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0300636792 -0.002017380 -0.0008925593
## Lag 2e+05 0.0150791552 -0.009122804 -0.0288135579
## Lag 3e+05 -0.0003032291 -0.009722598 -0.0149616597
## Lag 4e+05 0.0192007382 0.016900698 0.0035640685
## Lag 5e+05 -0.0227410332 -0.004199823 -0.0109229387
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005219392 0.023250794 -0.004937892
## Lag 2e+05 0.011332192 0.012948200 -0.026657161
## Lag 3e+05 -0.013193865 0.004780372 0.005862342
## Lag 4e+05 -0.014399964 0.028150625 0.009929328
## Lag 5e+05 0.014346074 -0.025629618 0.017369591
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.012501484
## Lag 2e+05 0.002732422
## Lag 3e+05 -0.005926844
## Lag 4e+05 0.036510532
## Lag 5e+05 -0.012708863
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.3275 -2.1777 0.0214
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.5890 -0.8254 -1.1016
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8803 0.1926 -0.7592
## absdiff.sqrt.age
## -0.5819
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.18435506 0.02942797 0.98292434
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.11206200 0.40917087 0.27063277
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.37871390 0.84723488 0.44773132
## absdiff.sqrt.age
## 0.56062253
## Joint P-value (lower = worse): 0.2891199 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.364236 0.436802 -0.251930
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.897960 -0.112579 -0.002702
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.245986 1.376119 -0.784950
## absdiff.sqrt.age
## 0.028548
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7156819 0.6622548 0.8010949
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3692067 0.9103640 0.9978444
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8056932 0.1687848 0.4324831
## absdiff.sqrt.age
## 0.9772252
## Joint P-value (lower = worse): 0.8827967 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.6407 0.9586 -1.6891
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.2732 0.8059 -0.1146
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.2465 0.4348 -0.2693
## absdiff.sqrt.age
## 0.3946
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.52171888 0.33775463 0.09120271
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.78471004 0.42028388 0.90878777
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.21257724 0.66367593 0.78771062
## absdiff.sqrt.age
## 0.69314732
## Joint P-value (lower = worse): 0.1910543 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7890 1.2221 -1.7042
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.1695 0.2208 0.8489
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.1499 1.4493 1.9785
## absdiff.sqrt.age
## 0.1967
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.43012481 0.22165883 0.08834964
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.86537594 0.82528423 0.39594956
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.25017549 0.14725060 0.04787553
## absdiff.sqrt.age
## 0.84408351
## Joint P-value (lower = worse): 0.3636991 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 2.0294 1.0103 -1.3629
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.2965 0.1624 0.2034
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.1608 1.1216 1.5055
## absdiff.sqrt.age
## 1.2263
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.04241846 0.31237014 0.17290991
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.19481520 0.87100672 0.83883526
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.24572014 0.26204655 0.13220562
## absdiff.sqrt.age
## 0.22008473
## Joint P-value (lower = worse): 0.2377963 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3757 -0.8571 -1.3871
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.4681 -0.9130 -0.2103
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9302 -0.7613 0.9851
## absdiff.sqrt.age
## 0.4068
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7071388 0.3913866 0.1654066
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.1420857 0.3612551 0.8334070
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3522914 0.4464877 0.3245971
## absdiff.sqrt.age
## 0.6841911
## Joint P-value (lower = worse): 0.5337654 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.1597 0.3011 -0.9576
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.5312 -0.1916 -0.2759
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.0039 -0.1191 1.0372
## absdiff.sqrt.age
## 0.3885
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8731123 0.7633553 0.3382472
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5952904 0.8480210 0.7826535
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3154118 0.9051800 0.2996273
## absdiff.sqrt.age
## 0.6976633
## Joint P-value (lower = worse): 0.8806737 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5718 -0.1604 -0.6994
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2117 1.0104 0.1837
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1305 0.5924 0.8583
## absdiff.sqrt.age
## 0.1324
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5674725 0.8725316 0.4842915
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8323300 0.3123027 0.8542439
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8961811 0.5535766 0.3907472
## absdiff.sqrt.age
## 0.8946793
## Joint P-value (lower = worse): 0.9678352 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 2.56020 58.391 0.33712 0.35249
## nodefactor.deg.main.1 1.74833 60.531 0.34948 0.35604
## nodefactor.race..wa.B -0.46277 19.538 0.11280 0.11922
## nodefactor.race..wa.H 0.34290 29.713 0.17155 0.18463
## nodefactor.region.EW 0.07413 23.503 0.13570 0.13976
## nodefactor.region.OW 2.03730 47.684 0.27530 0.28500
## concurrent 2.35600 52.251 0.30167 0.31542
## nodematch.race..wa.B -0.12498 2.958 0.01708 0.01791
## nodematch.race..wa.H 0.22560 7.394 0.04269 0.04895
## nodematch.race..wa.O 2.47709 44.361 0.25612 0.26483
## absdiff.sqrt.age 3.87738 57.449 0.33168 0.33551
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -111.50 -37.500 2.5000 42.500 116.50
## nodefactor.deg.main.1 -117.00 -39.000 2.0000 43.000 120.00
## nodefactor.race..wa.B -38.52 -13.517 -0.5168 12.483 38.48
## nodefactor.race..wa.H -57.34 -20.340 0.6600 20.660 58.66
## nodefactor.region.EW -45.59 -15.588 -0.5885 16.412 46.41
## nodefactor.region.OW -90.25 -30.255 1.7450 33.745 96.75
## concurrent -100.00 -33.000 2.0000 38.000 105.00
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.1815 4.819 15.82
## nodematch.race..wa.O -83.08 -28.081 1.9192 31.919 89.92
## absdiff.sqrt.age -106.95 -35.317 3.6019 42.581 117.23
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.81532396
## nodefactor.deg.main.1 0.81532396 1.00000000
## nodefactor.race..wa.B 0.40940550 0.31666461
## nodefactor.race..wa.H 0.53983925 0.48026138
## nodefactor.region.EW 0.46367902 0.37851973
## nodefactor.region.OW 0.73369985 0.56627424
## concurrent 0.95337514 0.77428823
## nodematch.race..wa.B 0.08308939 0.06171822
## nodematch.race..wa.H 0.16762233 0.16518989
## nodematch.race..wa.O 0.84248329 0.67197148
## absdiff.sqrt.age 0.84390304 0.68902688
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.40940550 0.53983925
## nodefactor.deg.main.1 0.31666461 0.48026138
## nodefactor.race..wa.B 1.00000000 0.18652647
## nodefactor.race..wa.H 0.18652647 1.00000000
## nodefactor.region.EW 0.14882036 0.35058572
## nodefactor.region.OW 0.27779864 0.37415021
## concurrent 0.39628243 0.52824275
## nodematch.race..wa.B 0.36149729 0.01141444
## nodematch.race..wa.H 0.01880424 0.56208942
## nodematch.race..wa.O 0.08861357 0.11072056
## absdiff.sqrt.age 0.34356660 0.45917886
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.46367902 0.73369985 0.95337514
## nodefactor.deg.main.1 0.37851973 0.56627424 0.77428823
## nodefactor.race..wa.B 0.14882036 0.27779864 0.39628243
## nodefactor.race..wa.H 0.35058572 0.37415021 0.52824275
## nodefactor.region.EW 1.00000000 0.21260844 0.43892589
## nodefactor.region.OW 0.21260844 1.00000000 0.69444399
## concurrent 0.43892589 0.69444399 1.00000000
## nodematch.race..wa.B 0.01808728 0.05140923 0.07919731
## nodematch.race..wa.H 0.13950353 0.11100574 0.16821251
## nodematch.race..wa.O 0.35349339 0.63647814 0.79437030
## absdiff.sqrt.age 0.39247342 0.62043941 0.80182799
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.08308939 0.167622327
## nodefactor.deg.main.1 0.06171822 0.165189888
## nodefactor.race..wa.B 0.36149729 0.018804236
## nodefactor.race..wa.H 0.01141444 0.562089421
## nodefactor.region.EW 0.01808728 0.139503533
## nodefactor.region.OW 0.05140923 0.111005737
## concurrent 0.07919731 0.168212515
## nodematch.race..wa.B 1.00000000 -0.012255626
## nodematch.race..wa.H -0.01225563 1.000000000
## nodematch.race..wa.O 0.01475949 0.006785681
## absdiff.sqrt.age 0.06875455 0.143950560
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.842483286 0.84390304
## nodefactor.deg.main.1 0.671971482 0.68902688
## nodefactor.race..wa.B 0.088613567 0.34356660
## nodefactor.race..wa.H 0.110720562 0.45917886
## nodefactor.region.EW 0.353493391 0.39247342
## nodefactor.region.OW 0.636478138 0.62043941
## concurrent 0.794370299 0.80182799
## nodematch.race..wa.B 0.014759487 0.06875455
## nodematch.race..wa.H 0.006785681 0.14395056
## nodematch.race..wa.O 1.000000000 0.70950037
## absdiff.sqrt.age 0.709500368 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.037286150 0.053973070 0.083178684
## Lag 2e+05 0.023799557 0.024959716 0.024622302
## Lag 3e+05 0.002384504 0.006625950 -0.019009190
## Lag 4e+05 0.001583185 -0.002520842 -0.006808975
## Lag 5e+05 -0.014736850 -0.004921189 -0.019600725
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.064708674 0.025235989 0.024533318
## Lag 2e+05 0.010123800 -0.023004175 0.019214796
## Lag 3e+05 0.013486204 -0.009496313 0.009793142
## Lag 4e+05 0.005749365 -0.017052115 0.030921503
## Lag 5e+05 0.003898799 0.005610631 -0.019661396
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 0.037093337 4.081248e-02 0.129533238
## Lag 2e+05 0.004351162 3.644228e-03 0.045049292
## Lag 3e+05 0.008055477 1.653099e-02 0.031994256
## Lag 4e+05 0.004681985 -1.177319e-03 0.015435097
## Lag 5e+05 -0.014824707 -7.333728e-06 0.008942917
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.01814904 0.002137389
## Lag 2e+05 0.01712617 0.011427675
## Lag 3e+05 0.01077021 -0.018317130
## Lag 4e+05 0.01235665 0.011996843
## Lag 5e+05 -0.02694711 0.004830735
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.029810042 0.0247548076 0.062691605
## Lag 2e+05 0.001266106 0.0033180904 0.015261145
## Lag 3e+05 0.036104133 0.0099935405 0.026363561
## Lag 4e+05 0.002473999 -0.0054341546 -0.005960574
## Lag 5e+05 0.004890703 0.0002319202 -0.005521103
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.043075754 0.0426253015 0.045429100
## Lag 2e+05 0.024111558 -0.0028820058 -0.008053735
## Lag 3e+05 -0.004511711 0.0154625712 0.036576616
## Lag 4e+05 0.001945730 -0.0112721333 0.006491514
## Lag 5e+05 -0.001830603 0.0001993954 0.001757440
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.033181117 0.06751044 0.11669525
## Lag 2e+05 -0.002665167 0.00537341 0.01832427
## Lag 3e+05 0.031986078 0.01777450 0.01481518
## Lag 4e+05 0.008663433 -0.01545655 0.01465160
## Lag 5e+05 0.000642860 -0.01476121 -0.01128883
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.036241834 0.017711444
## Lag 2e+05 0.005172482 0.005439962
## Lag 3e+05 0.037093887 0.015267689
## Lag 4e+05 0.015096004 0.004385634
## Lag 5e+05 0.026496799 0.004792809
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.045994832 0.029266468 0.070185868
## Lag 2e+05 0.008519233 -0.005950597 0.022486965
## Lag 3e+05 -0.006237169 -0.007808244 -0.017282285
## Lag 4e+05 0.029173872 0.030815002 -0.010547668
## Lag 5e+05 0.013070976 -0.003110699 -0.005951494
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.063722769 0.024333436 0.024118209
## Lag 2e+05 0.035904931 0.009959550 -0.002207011
## Lag 3e+05 0.021359563 0.023246275 -0.005401745
## Lag 4e+05 0.006659831 -0.006364673 0.017542774
## Lag 5e+05 -0.005102857 -0.002660498 0.002294958
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.051655639 0.026600675 0.121305968
## Lag 2e+05 0.014867896 0.007572277 0.034444056
## Lag 3e+05 0.005346994 0.001751777 -0.005108472
## Lag 4e+05 0.030733697 -0.005029932 0.007177923
## Lag 5e+05 0.008712063 -0.005668931 -0.004320882
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.028080372 0.01752561
## Lag 2e+05 0.013215180 -0.01276761
## Lag 3e+05 -0.008126697 -0.01992698
## Lag 4e+05 0.001219329 0.01060865
## Lag 5e+05 0.009198453 0.00631485
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.046979438 0.059636063 0.035060817
## Lag 2e+05 -0.009646163 -0.013474128 0.001079246
## Lag 3e+05 0.003323737 -0.011176088 0.019682855
## Lag 4e+05 -0.023260995 -0.028660818 0.028032824
## Lag 5e+05 0.007204432 -0.007629623 0.004375414
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.0591349911 0.018943004 1.950200e-02
## Lag 2e+05 -0.0004480031 -0.002725787 -2.019611e-02
## Lag 3e+05 -0.0042947580 0.027014547 9.317803e-05
## Lag 4e+05 -0.0150846125 -0.007041113 -9.807235e-03
## Lag 5e+05 0.0048232534 -0.006120898 8.660884e-03
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0444343249 0.053761175 0.110710492
## Lag 2e+05 -0.0062045059 -0.013456352 0.032585937
## Lag 3e+05 0.0004976362 -0.011842201 -0.006273306
## Lag 4e+05 -0.0249906751 -0.003846662 0.007270896
## Lag 5e+05 0.0050188098 -0.010614518 -0.012300748
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.039635059 -0.000505848
## Lag 2e+05 0.009374158 -0.012724647
## Lag 3e+05 0.007849791 -0.014672800
## Lag 4e+05 -0.005207145 -0.014733071
## Lag 5e+05 -0.006241605 0.012576622
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.069829312 0.041938982 0.04184465
## Lag 2e+05 -0.005701886 -0.017335699 0.02578281
## Lag 3e+05 0.015282344 -0.006897152 0.01425194
## Lag 4e+05 0.009266438 0.029771024 0.01811135
## Lag 5e+05 -0.013286560 -0.009683505 0.01965237
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.077564787 0.066482959 0.03675020
## Lag 2e+05 0.016836957 0.017137486 -0.01972807
## Lag 3e+05 0.012654103 -0.023918516 0.01438247
## Lag 4e+05 0.009452725 -0.008100835 0.03091076
## Lag 5e+05 -0.004527649 0.004439086 -0.00152464
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.066462975 0.044381718 0.1286658329
## Lag 2e+05 -0.013796907 -0.015332947 0.0221688051
## Lag 3e+05 0.006346225 0.022774605 0.0025354422
## Lag 4e+05 -0.001672189 0.009221063 0.0090288603
## Lag 5e+05 -0.010621236 -0.002448240 -0.0005961641
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.060382109 0.046926106
## Lag 2e+05 -0.016637481 -0.010075757
## Lag 3e+05 0.017354635 0.014745149
## Lag 4e+05 0.007179554 0.007226805
## Lag 5e+05 -0.016321773 -0.008523600
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.038116307 0.0256461226 0.050785980
## Lag 2e+05 -0.010911659 -0.0095280823 -0.012031487
## Lag 3e+05 -0.017010026 0.0001268855 -0.018595993
## Lag 4e+05 0.010620751 0.0122613897 0.006962936
## Lag 5e+05 -0.001511979 0.0024015302 -0.007845244
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.065610296 0.039242456 0.053204137
## Lag 2e+05 0.006331961 0.001917704 0.008586125
## Lag 3e+05 0.005067413 -0.015394618 -0.008686808
## Lag 4e+05 -0.008404839 0.010967022 0.009468107
## Lag 5e+05 0.012183647 -0.031621516 -0.020683961
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0487649215 0.070290825 0.1385022836
## Lag 2e+05 -0.0153537173 -0.001692911 0.0145275078
## Lag 3e+05 -0.0026966517 -0.015234988 0.0105130579
## Lag 4e+05 0.0035381043 -0.011034684 -0.0080989193
## Lag 5e+05 -0.0007243528 0.011069148 -0.0002954398
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.036937039 -0.003971490
## Lag 2e+05 0.015793976 0.016749882
## Lag 3e+05 -0.007192037 -0.006522373
## Lag 4e+05 -0.009100066 0.011594334
## Lag 5e+05 -0.021438329 0.002404723
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.078280610 0.070802879 0.0328512933
## Lag 2e+05 -0.025087534 -0.019529753 0.0009767469
## Lag 3e+05 0.015938364 0.007005649 -0.0159548415
## Lag 4e+05 0.004576905 -0.012997840 -0.0101964851
## Lag 5e+05 0.015553809 -0.007698634 -0.0117533496
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.097424481 0.057011262 0.0534904598
## Lag 2e+05 0.008153905 0.022433953 -0.0287604036
## Lag 3e+05 0.025298946 0.017477612 0.0009888269
## Lag 4e+05 0.003954406 0.003167544 0.0081740436
## Lag 5e+05 -0.013938974 -0.016393943 0.0318674514
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.074061586 0.05705956 0.126969352
## Lag 2e+05 -0.023538252 -0.02992377 0.026454998
## Lag 3e+05 0.020286598 -0.00794342 -0.009863259
## Lag 4e+05 -0.001837354 -0.01674762 -0.016449983
## Lag 5e+05 0.001728648 -0.02388094 -0.005241626
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.049047685 0.040284976
## Lag 2e+05 -0.028003094 -0.009788137
## Lag 3e+05 -0.006704590 0.018804934
## Lag 4e+05 0.001765729 0.007836237
## Lag 5e+05 0.021771202 0.006542356
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.014492698 0.025203998 0.038957303
## Lag 2e+05 -0.001342423 0.009104508 -0.004216668
## Lag 3e+05 -0.010031467 -0.032178336 0.017529350
## Lag 4e+05 -0.017283374 -0.015368051 0.024523450
## Lag 5e+05 0.013102579 0.018108061 0.009978828
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.06438740 -0.004589530 0.039568590
## Lag 2e+05 0.02702030 0.008517797 0.004413620
## Lag 3e+05 -0.01772278 0.010960372 -0.015493205
## Lag 4e+05 -0.03611078 -0.015312436 -0.004144681
## Lag 5e+05 0.04106608 0.006097113 0.023200539
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.017398731 0.049210221 0.128279635
## Lag 2e+05 0.004721312 0.003032944 0.043719388
## Lag 3e+05 -0.009112096 -0.001167766 0.038539044
## Lag 4e+05 -0.016400998 -0.018298393 -0.008672095
## Lag 5e+05 0.004889841 -0.013433095 0.019190510
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0005281401 -0.007672802
## Lag 2e+05 -0.0076028553 0.012468414
## Lag 3e+05 0.0154958465 -0.005035327
## Lag 4e+05 -0.0017618691 -0.006923323
## Lag 5e+05 -0.0203466309 0.018639543
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.95086 -0.85572 -2.35064
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.22592 0.05783 -0.28661
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.56666 0.45455 1.15390
## nodematch.race..wa.O absdiff.sqrt.age
## -0.44587 -0.04987
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.34167527 0.39215465 0.01874106
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.82126216 0.95388023 0.77440863
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.57094184 0.64943362 0.24854129
## nodematch.race..wa.O absdiff.sqrt.age
## 0.65569447 0.96022224
## Joint P-value (lower = worse): 0.1724949 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.25537 0.23737 0.56534
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.07279 -0.52720 0.31301
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.53958 -0.32990 -1.75500
## nodematch.race..wa.O absdiff.sqrt.age
## -0.02794 -0.02963
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7984390 0.8123734 0.5718442
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9419732 0.5980539 0.7542742
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.5894876 0.7414735 0.0792598
## nodematch.race..wa.O absdiff.sqrt.age
## 0.9777087 0.9763628
## Joint P-value (lower = worse): 0.8106185 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.8307 -1.9603 -0.4892
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.7056 -0.4852 -2.6789
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.4575 -0.5368 1.0163
## nodematch.race..wa.O absdiff.sqrt.age
## -1.9648 -1.9387
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.06714026 0.04996150 0.62467897
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.48042409 0.62753830 0.00738554
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.14498322 0.59139726 0.30949636
## nodematch.race..wa.O absdiff.sqrt.age
## 0.04943181 0.05253321
## Joint P-value (lower = worse): 0.1388281 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.54246 0.01329 0.75142
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.19336 0.51226 0.54583
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.73670 -2.05268 -0.72858
## nodematch.race..wa.O absdiff.sqrt.age
## 0.32310 0.44429
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.58750075 0.98939541 0.45240186
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.84667823 0.60846775 0.58518139
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.46130479 0.04010362 0.46625914
## nodematch.race..wa.O absdiff.sqrt.age
## 0.74661640 0.65683298
## Joint P-value (lower = worse): 0.5998682 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.7703 -0.7951 -0.1447
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.4544 1.0915 -0.6894
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.0161 -1.7074 1.3674
## nodematch.race..wa.O absdiff.sqrt.age
## -1.0228 0.1336
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.44114243 0.42658118 0.88491260
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.64953547 0.27506236 0.49057987
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.98715684 0.08774392 0.17150397
## nodematch.race..wa.O absdiff.sqrt.age
## 0.30639226 0.89370692
## Joint P-value (lower = worse): 0.1115582 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.7432 -0.4806 -1.8145
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3513 0.4665 0.3618
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.4552 -1.6340 0.3979
## nodematch.race..wa.O absdiff.sqrt.age
## -0.4570 -0.5523
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.45735013 0.63080676 0.06959447
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.72533686 0.64084048 0.71747586
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.64897426 0.10226746 0.69068901
## nodematch.race..wa.O absdiff.sqrt.age
## 0.64768824 0.58074698
## Joint P-value (lower = worse): 0.5455631 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.94331 1.51127 -0.07321
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.59393 -0.30278 0.77654
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.92448 -0.17841 -0.26814
## nodematch.race..wa.O absdiff.sqrt.age
## 1.48486 1.31954
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3455200 0.1307198 0.9416429
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5525599 0.7620547 0.4374317
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3552371 0.8584000 0.7885940
## nodematch.race..wa.O absdiff.sqrt.age
## 0.1375820 0.1869872
## Joint P-value (lower = worse): 0.8577423 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3334 -0.3222 -1.0021
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.3851 -1.0456 -0.1218
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.5332 0.3746 -0.2185
## nodematch.race..wa.O absdiff.sqrt.age
## 0.1865 0.2666
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7388402 0.7473110 0.3163088
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7001695 0.2957361 0.9030921
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.5939285 0.7079896 0.8270315
## nodematch.race..wa.O absdiff.sqrt.age
## 0.8520545 0.7897682
## Joint P-value (lower = worse): 0.9469402 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.66680 58.448 0.33745 0.36719
## nodefactor.deg.main.1 -0.28323 60.598 0.34987 0.39071
## nodefactor.race..wa.B 0.45133 19.551 0.11288 0.12474
## nodefactor.race..wa.H -0.40497 29.565 0.17069 0.20151
## nodefactor.region.EW -0.35553 29.095 0.16798 0.22619
## nodefactor.region.OW 1.52177 58.429 0.33734 0.38532
## concurrent 0.65537 52.479 0.30299 0.33920
## nodematch.race..wa.B 0.01225 2.959 0.01709 0.01936
## nodematch.race..wa.H -0.11196 7.367 0.04254 0.05847
## nodematch.race..wa.O 0.62632 44.390 0.25629 0.28304
## nodematch.region 0.79673 50.110 0.28931 0.32255
## absdiff.sqrt.age 0.23514 57.200 0.33024 0.34861
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -113.50 -38.500 0.50000 40.500 115.50
## nodefactor.deg.main.1 -117.00 -41.000 0.00000 40.000 120.00
## nodefactor.race..wa.B -37.52 -12.517 0.48320 13.483 39.48
## nodefactor.race..wa.H -59.34 -20.340 -0.34000 19.660 57.66
## nodefactor.region.EW -56.59 -20.588 -0.58850 19.412 58.41
## nodefactor.region.OW -112.25 -38.255 0.74500 40.745 115.75
## concurrent -101.00 -35.000 0.00000 36.000 104.00
## nodematch.race..wa.B -5.48 -2.480 -0.47985 1.520 6.52
## nodematch.race..wa.H -14.18 -5.181 -0.18150 4.819 14.82
## nodematch.race..wa.O -86.08 -29.081 -0.08078 29.919 88.92
## nodematch.region -97.00 -33.000 1.00000 34.000 99.00
## absdiff.sqrt.age -111.00 -38.218 -0.42208 38.203 114.40
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.81456784
## nodefactor.deg.main.1 0.81456784 1.00000000
## nodefactor.race..wa.B 0.40935667 0.30881917
## nodefactor.race..wa.H 0.53767302 0.47318457
## nodefactor.region.EW 0.38372755 0.31293465
## nodefactor.region.OW 0.61476344 0.44840452
## concurrent 0.95340527 0.77306159
## nodematch.race..wa.B 0.07765574 0.05024095
## nodematch.race..wa.H 0.16467644 0.15594565
## nodematch.race..wa.O 0.84442801 0.67789983
## nodematch.region 0.93038530 0.76251573
## absdiff.sqrt.age 0.84574946 0.68769371
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.40935667 0.53767302
## nodefactor.deg.main.1 0.30881917 0.47318457
## nodefactor.race..wa.B 1.00000000 0.18166470
## nodefactor.race..wa.H 0.18166470 1.00000000
## nodefactor.region.EW 0.09764293 0.34586107
## nodefactor.region.OW 0.21743022 0.31827692
## concurrent 0.39614937 0.52278941
## nodematch.race..wa.B 0.36437062 0.01266853
## nodematch.race..wa.H 0.02020527 0.56056088
## nodematch.race..wa.O 0.09229356 0.11163319
## nodematch.region 0.39016704 0.48281919
## absdiff.sqrt.age 0.34885504 0.45685588
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.383727554 0.61476344 0.95340527
## nodefactor.deg.main.1 0.312934654 0.44840452 0.77306159
## nodefactor.race..wa.B 0.097642931 0.21743022 0.39614937
## nodefactor.race..wa.H 0.345861071 0.31827692 0.52278941
## nodefactor.region.EW 1.000000000 0.11159336 0.36057873
## nodefactor.region.OW 0.111593364 1.00000000 0.57699309
## concurrent 0.360578729 0.57699309 1.00000000
## nodematch.race..wa.B 0.006754638 0.03740027 0.07665564
## nodematch.race..wa.H 0.164992910 0.10041914 0.16434475
## nodematch.race..wa.O 0.273051536 0.53839931 0.79818692
## nodematch.region 0.253396240 0.53932461 0.88909544
## absdiff.sqrt.age 0.325989762 0.52057949 0.80432294
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.077655738 0.164676442
## nodefactor.deg.main.1 0.050240951 0.155945653
## nodefactor.race..wa.B 0.364370624 0.020205271
## nodefactor.race..wa.H 0.012668525 0.560560879
## nodefactor.region.EW 0.006754638 0.164992910
## nodefactor.region.OW 0.037400274 0.100419144
## concurrent 0.076655640 0.164344753
## nodematch.race..wa.B 1.000000000 0.002924091
## nodematch.race..wa.H 0.002924091 1.000000000
## nodematch.race..wa.O 0.006223547 0.003378771
## nodematch.region 0.073980255 0.143059365
## absdiff.sqrt.age 0.064027496 0.137716482
## nodematch.race..wa.O nodematch.region
## edges 0.844428009 0.93038530
## nodefactor.deg.main.1 0.677899825 0.76251573
## nodefactor.race..wa.B 0.092293565 0.39016704
## nodefactor.race..wa.H 0.111633191 0.48281919
## nodefactor.region.EW 0.273051536 0.25339624
## nodefactor.region.OW 0.538399314 0.53932461
## concurrent 0.798186920 0.88909544
## nodematch.race..wa.B 0.006223547 0.07398025
## nodematch.race..wa.H 0.003378771 0.14305937
## nodematch.race..wa.O 1.000000000 0.79153679
## nodematch.region 0.791536788 1.00000000
## absdiff.sqrt.age 0.711535111 0.78584345
## absdiff.sqrt.age
## edges 0.8457495
## nodefactor.deg.main.1 0.6876937
## nodefactor.race..wa.B 0.3488550
## nodefactor.race..wa.H 0.4568559
## nodefactor.region.EW 0.3259898
## nodefactor.region.OW 0.5205795
## concurrent 0.8043229
## nodematch.race..wa.B 0.0640275
## nodematch.race..wa.H 0.1377165
## nodematch.race..wa.O 0.7115351
## nodematch.region 0.7858435
## absdiff.sqrt.age 1.0000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.074293832 0.103184510 0.099417520
## Lag 2e+05 0.003235864 0.007750902 0.032188678
## Lag 3e+05 0.019147412 0.009635380 0.006955118
## Lag 4e+05 0.017612327 0.027764309 0.011395681
## Lag 5e+05 -0.006474480 -0.007160272 -0.023443624
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.14509585 0.207746950 0.136487350
## Lag 2e+05 0.03518996 0.084236194 0.030927399
## Lag 3e+05 0.03735473 0.042162927 0.016833797
## Lag 4e+05 -0.01440172 -0.003179383 0.006021293
## Lag 5e+05 -0.01985848 -0.017586317 0.007254426
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.085703515 0.101190378 0.232560491
## Lag 2e+05 0.011717952 0.014873165 0.085772228
## Lag 3e+05 0.006315926 -0.017897670 0.050480378
## Lag 4e+05 0.007808545 0.001998121 -0.004240963
## Lag 5e+05 -0.003418756 -0.012928698 0.012982839
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.073623014 0.108060165 0.013346907
## Lag 2e+05 0.012916930 0.027019565 0.003174847
## Lag 3e+05 -0.009078349 0.016055797 0.004733757
## Lag 4e+05 0.023761772 -0.005430556 0.006818297
## Lag 5e+05 0.002785007 0.002187026 -0.017992580
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.115223816 0.125460243 0.1193641005
## Lag 2e+05 0.009324493 0.025916692 0.0154064194
## Lag 3e+05 0.012966545 0.014875380 -0.0009208817
## Lag 4e+05 0.024082652 0.005666413 0.0077636945
## Lag 5e+05 0.032722502 -0.003934597 0.0180051689
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.15064462 0.22376202 0.152869891
## Lag 2e+05 0.04103333 0.11661253 0.017991300
## Lag 3e+05 0.02424551 0.04612830 -0.005044468
## Lag 4e+05 0.01232480 0.03524642 0.012214286
## Lag 5e+05 0.01802221 0.03528585 0.011701918
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.125260272 0.08480411 0.24172748
## Lag 2e+05 0.009617682 0.03928150 0.09046472
## Lag 3e+05 0.008979788 -0.01032875 0.04097252
## Lag 4e+05 0.024174416 -0.01563746 0.04167515
## Lag 5e+05 0.038257803 -0.02714986 0.04166747
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.102373798 0.130918465 0.070129431
## Lag 2e+05 0.005066625 -0.002073079 0.002905888
## Lag 3e+05 0.006226339 0.022411360 -0.011380755
## Lag 4e+05 0.038630460 0.016027926 0.010600987
## Lag 5e+05 0.054250347 0.030869334 0.011872936
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.087882032 0.091039267 0.121189900
## Lag 2e+05 0.020945280 0.041463336 0.021004095
## Lag 3e+05 0.004190995 0.010480954 -0.028531850
## Lag 4e+05 0.003060805 -0.006696759 -0.007880029
## Lag 5e+05 0.021879214 0.021980128 -0.005641812
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.131896432 0.22849262 0.104251287
## Lag 2e+05 0.045359576 0.10513004 0.015184035
## Lag 3e+05 -0.003647636 0.08003565 -0.035096920
## Lag 4e+05 0.010741760 0.03931201 -0.003852028
## Lag 5e+05 0.037280635 0.02563615 -0.012858144
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.102314640 0.077471881 0.28060387
## Lag 2e+05 0.034863788 0.033003690 0.09379429
## Lag 3e+05 0.010830524 0.003756270 0.03919141
## Lag 4e+05 -0.007357862 0.006302403 0.04841501
## Lag 5e+05 0.020597082 0.005428096 0.05800565
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.069197071 0.120046628 0.0428075022
## Lag 2e+05 0.017875164 0.015370300 0.0238138148
## Lag 3e+05 -0.004151606 -0.004578317 -0.0100426214
## Lag 4e+05 -0.011063107 0.016452838 -0.0062286812
## Lag 5e+05 0.003506228 0.015527336 -0.0002153367
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.09965993 0.085906354 0.095825777
## Lag 2e+05 0.02288362 0.013038705 0.027394229
## Lag 3e+05 0.01500735 0.037029784 -0.002300478
## Lag 4e+05 -0.01560653 -0.022260062 0.008738308
## Lag 5e+05 0.01242791 -0.001170029 0.009693686
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.0000000000
## Lag 1e+05 0.17597384 0.23764473 0.1184861441
## Lag 2e+05 0.05671462 0.12191994 0.0214990675
## Lag 3e+05 0.02751840 0.04664070 0.0135271955
## Lag 4e+05 0.02177744 0.04419933 -0.0006331608
## Lag 5e+05 0.01959448 0.03671348 0.0257683669
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.112858636 0.094418845 0.26739887
## Lag 2e+05 0.021544028 0.056460475 0.08951689
## Lag 3e+05 0.013182985 0.006552299 0.05954811
## Lag 4e+05 -0.012851059 0.001760424 0.05222515
## Lag 5e+05 0.002607713 -0.033753878 0.01679016
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.000000e+00
## Lag 1e+05 0.07490300 0.126989905 6.148455e-02
## Lag 2e+05 0.01575161 0.026939046 2.384938e-02
## Lag 3e+05 0.01368416 0.026676327 6.261880e-03
## Lag 4e+05 -0.01814433 -0.007087429 -6.825193e-05
## Lag 5e+05 0.01411661 0.009143597 4.661058e-03
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.095829257 0.099756248 0.08681252
## Lag 2e+05 0.013053650 0.009667583 -0.02029464
## Lag 3e+05 -0.012695800 -0.001958401 0.01147690
## Lag 4e+05 -0.015301872 -0.008372395 -0.01105644
## Lag 5e+05 0.005846601 0.007889433 -0.01003685
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.149022359 0.21477377 0.12964445
## Lag 2e+05 0.058485212 0.10335735 0.03305176
## Lag 3e+05 0.023253319 0.05815442 -0.02474144
## Lag 4e+05 -0.005692117 0.01785135 -0.03134798
## Lag 5e+05 0.023873202 0.01607740 -0.01074687
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.108027150 0.120269981 0.22619595
## Lag 2e+05 0.016543501 0.014070368 0.07723189
## Lag 3e+05 0.003252081 -0.022820270 0.03353427
## Lag 4e+05 -0.001535082 0.006914825 0.02678001
## Lag 5e+05 0.008407682 -0.025428426 0.01978278
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.100896695 0.13316347 0.062489591
## Lag 2e+05 0.013927347 0.01350103 0.004510762
## Lag 3e+05 -0.004561992 -0.02151337 -0.006091791
## Lag 4e+05 -0.003427188 -0.02026785 -0.005896213
## Lag 5e+05 -0.006602846 -0.00211587 0.015244832
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.110822089 0.104475352 0.116854618
## Lag 2e+05 0.032670686 0.037658351 0.041640538
## Lag 3e+05 0.012880414 0.017445646 0.009049369
## Lag 4e+05 0.006163556 -0.012091604 -0.022953106
## Lag 5e+05 0.018532449 0.002957128 0.016554608
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.149949333 0.24470239 0.144931494
## Lag 2e+05 0.043272434 0.11025784 0.024020775
## Lag 3e+05 0.016379884 0.08454198 0.006030983
## Lag 4e+05 0.008830322 0.04888941 0.002409433
## Lag 5e+05 0.011196342 -0.01007933 0.017713875
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.118290139 0.130864654 0.24216763
## Lag 2e+05 0.028463444 0.022262901 0.08805779
## Lag 3e+05 0.016152060 -0.005293691 0.05253961
## Lag 4e+05 0.009733243 -0.005783728 0.03662347
## Lag 5e+05 0.009080203 -0.006199656 0.02953350
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.090577207 0.12445116 0.0539571400
## Lag 2e+05 0.031937051 0.03135497 -0.0158199301
## Lag 3e+05 0.006908005 0.02599254 0.0119028066
## Lag 4e+05 0.028890178 0.00485657 0.0006412076
## Lag 5e+05 0.006978902 0.02268425 0.0102243027
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.119655203 0.11629333 0.096200024
## Lag 2e+05 0.012186792 0.02028062 -0.005461918
## Lag 3e+05 -0.007695295 -0.01257345 0.008225973
## Lag 4e+05 -0.021553244 -0.02948419 -0.014846870
## Lag 5e+05 -0.018705821 -0.02577632 -0.006179736
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.15048807 0.248343014 0.16286658
## Lag 2e+05 0.03528786 0.076380143 0.05595228
## Lag 3e+05 -0.02799785 0.042653199 0.03046889
## Lag 4e+05 -0.03213655 0.016475017 0.02174683
## Lag 5e+05 -0.02936321 0.009942162 0.01197120
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.119063415 0.078827405 0.223165480
## Lag 2e+05 0.023225961 0.035913827 0.084158034
## Lag 3e+05 -0.002429621 0.008113151 0.015522006
## Lag 4e+05 -0.019807585 -0.014222378 0.006265332
## Lag 5e+05 -0.020237895 0.010747700 0.001661586
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.081664055 0.13986094 0.0713530831
## Lag 2e+05 -0.001422855 0.01114823 -0.0001825523
## Lag 3e+05 -0.004998221 -0.02907384 0.0034548259
## Lag 4e+05 -0.004114214 -0.03444100 -0.0214795508
## Lag 5e+05 0.007288660 -0.02503555 -0.0160214387
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.093343037 0.078285568 0.078901600
## Lag 2e+05 0.040636377 0.027985531 0.016820382
## Lag 3e+05 0.001246276 0.023158699 0.005714428
## Lag 4e+05 -0.005815376 -0.008688396 -0.007131129
## Lag 5e+05 -0.007561410 -0.003487521 -0.010611769
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.132503848 0.231695399 0.134358726
## Lag 2e+05 0.040672340 0.097945003 0.013834241
## Lag 3e+05 -0.013528752 0.019039257 0.002100438
## Lag 4e+05 -0.004497829 0.004624715 0.000404643
## Lag 5e+05 0.010497644 0.013923770 0.004361277
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.102417511 0.078739115 0.22207289
## Lag 2e+05 0.028443227 0.044502596 0.09783127
## Lag 3e+05 0.001307577 0.001499173 0.03131178
## Lag 4e+05 -0.007274084 0.032452400 0.01774579
## Lag 5e+05 -0.010498868 0.033350757 0.01915487
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.089375846 0.12369334 0.064247760
## Lag 2e+05 0.045304306 0.05186764 0.014606909
## Lag 3e+05 -0.013031036 0.01682722 0.017017466
## Lag 4e+05 -0.006798134 -0.01323113 0.015966310
## Lag 5e+05 -0.021934717 -0.01782833 0.007989782
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.4609 -1.0715 0.1384
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.1733 -0.2985 -0.9342
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.2804 1.9406 -1.4466
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.5649 -0.8217 -0.6377
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.64487253 0.28395647 0.88993828
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.86245326 0.76530043 0.35019126
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.77918346 0.05230778 0.14801322
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.57212503 0.41125800 0.52369479
## Joint P-value (lower = worse): 0.3054153 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.7290 1.3571 -0.1744
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.1600 0.4552 0.8827
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 1.8108 0.9515 -0.3113
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 1.2210 1.5909 1.2676
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08380489 0.17475429 0.86155823
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.24604230 0.64894109 0.37741861
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.07017914 0.34132868 0.75555829
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.22209196 0.11162970 0.20494271
## Joint P-value (lower = worse): 0.6623661 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6686 0.6608 1.4712
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5310 -0.1524 0.1810
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9611 0.7799 -0.1682
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.2012 0.5015 -0.3641
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5037695 0.5087711 0.1412246
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5954229 0.8789045 0.8563528
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3365032 0.4354553 0.8664026
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.8405243 0.6160452 0.7157900
## Joint P-value (lower = worse): 0.8041529 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.9484 -1.5226 0.9036
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.1150 -0.9920 -0.7184
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.3198 0.8713 -0.6261
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.7878 -1.0664 -1.3658
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3429310 0.1278636 0.3661999
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2648709 0.3212203 0.4725249
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.1868865 0.3835701 0.5312710
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4308254 0.2862503 0.1720174
## Joint P-value (lower = worse): 0.637911 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.2413 -0.5338 -1.1720
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -2.0669 -1.1867 0.7389
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.6866 -0.4951 -1.7282
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.8545 -0.6194 0.1275
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.80935013 0.59350570 0.24117757
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.03874792 0.23535837 0.45999548
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.49235589 0.62049989 0.08395362
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.39280043 0.53566661 0.89855936
## Joint P-value (lower = worse): 0.380919 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.78487 0.50761 0.40398
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.81492 0.07568 0.96900
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.85771 0.25023 0.62921
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.31261 0.50497 1.23779
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4325296 0.6117286 0.6862299
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.4151166 0.9396727 0.3325436
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3910545 0.8024123 0.5292140
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.7545755 0.6135768 0.2157949
## Joint P-value (lower = worse): 0.9785587 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8311 0.6403 0.5042
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8659 0.3932 1.6586
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.7850 -2.2687 1.2192
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.1300 0.5541 0.9714
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.40594493 0.52199430 0.61409291
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.38654681 0.69414037 0.09720191
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.43242436 0.02328945 0.22278489
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.89658071 0.57954032 0.33133755
## Joint P-value (lower = worse): 0.2502095 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.27638 -0.38847 0.40707
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.28886 0.65683 -1.28733
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.06285 -0.46211 -0.23880
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.79642 -0.53646 -1.28862
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7822535 0.6976688 0.6839559
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7726886 0.5112927 0.1979791
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9498845 0.6440026 0.8112637
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4257883 0.5916400 0.1975302
## Joint P-value (lower = worse): 0.17804 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.p.buildup.bal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x56055889d390>
##
## Iterations: 80 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.92022 0.02474 0 <1e-04 ***
## deg3+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 2
summary(est.p.buildup.bal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x56057c80b748>
##
## Iterations: 86 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.06381 0.03026 0 < 1e-04 ***
## nodefactor.race..wa.B 0.24745 0.06626 0 0.000188 ***
## nodefactor.race..wa.H 0.45289 0.04864 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 3
summary(est.p.buildup.bal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x56059a974c40>
##
## Iterations: 95 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.5470 0.1530 0 < 1e-04 ***
## nodefactor.race..wa.B 0.6620 0.1380 0 < 1e-04 ***
## nodefactor.race..wa.H 0.8720 0.1462 0 < 1e-04 ***
## nodematch.race..wa.B -0.5210 0.3745 0 0.16413
## nodematch.race..wa.H -0.2335 0.2066 0 0.25853
## nodematch.race..wa.O 0.5010 0.1550 0 0.00122 **
## deg3+ -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.0000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 4
summary(est.p.buildup.bal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x5605b8c3fc50>
##
## Iterations: 99 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.42309 0.15666 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.14241 0.03402 0 < 1e-04 ***
## nodefactor.race..wa.B 0.64936 0.13959 0 < 1e-04 ***
## nodefactor.race..wa.H 0.88531 0.14768 0 < 1e-04 ***
## nodematch.race..wa.B -0.52073 0.37622 0 0.16632
## nodematch.race..wa.H -0.23081 0.20857 0 0.26845
## nodematch.race..wa.O 0.49933 0.15653 0 0.00142 **
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 5
summary(est.p.buildup.bal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x5605d6fd3660>
##
## Iterations: 83 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.22601 0.15921 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.16025 0.03412 0 < 1e-04 ***
## nodefactor.race..wa.B 0.62238 0.13904 0 < 1e-04 ***
## nodefactor.race..wa.H 0.90832 0.14718 0 < 1e-04 ***
## nodefactor.region.EW -0.25682 0.05965 0 < 1e-04 ***
## nodefactor.region.OW -0.21613 0.03732 0 < 1e-04 ***
## nodematch.race..wa.B -0.52022 0.37640 0 0.16694
## nodematch.race..wa.H -0.23307 0.20954 0 0.26602
## nodematch.race..wa.O 0.50157 0.15610 0 0.00131 **
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 6
summary(est.p.buildup.bal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x5605f549b5a0>
##
## Iterations: 87 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.67402 0.16039 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.15920 0.03398 0 < 1e-04 ***
## nodefactor.race..wa.B 0.62469 0.13884 0 < 1e-04 ***
## nodefactor.race..wa.H 0.90777 0.14674 0 < 1e-04 ***
## nodefactor.region.EW -0.25784 0.05961 0 < 1e-04 ***
## nodefactor.region.OW -0.21543 0.03763 0 < 1e-04 ***
## nodematch.race..wa.B -0.51827 0.37492 0 0.16686
## nodematch.race..wa.H -0.23231 0.20782 0 0.26363
## nodematch.race..wa.O 0.50048 0.15516 0 0.00126 **
## absdiff.sqrt.age -0.56588 0.03254 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 7
summary(est.p.buildup.bal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") +
## degrange(from = 3) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x560613a2fa20>
##
## Iterations: 89 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.54354 0.16846 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.11233 0.02875 0 < 1e-04 ***
## nodefactor.race..wa.B 0.56471 0.13504 0 < 1e-04 ***
## nodefactor.race..wa.H 0.76359 0.14550 0 < 1e-04 ***
## nodefactor.region.EW -0.18174 0.04967 0 0.000253 ***
## nodefactor.region.OW -0.15146 0.03175 0 < 1e-04 ***
## concurrent 2.49735 0.06359 0 < 1e-04 ***
## nodematch.race..wa.B -0.52043 0.37590 0 0.166211
## nodematch.race..wa.H -0.23235 0.20670 0 0.260981
## nodematch.race..wa.O 0.50042 0.15633 0 0.001369 **
## absdiff.sqrt.age -0.54168 0.03245 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 8
summary(est.p.buildup.bal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x56063208bc90>
##
## Iterations: 82 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -13.03488 0.17484 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.11231 0.02877 0 < 1e-04 ***
## nodefactor.race..wa.B 0.59283 0.13447 0 < 1e-04 ***
## nodefactor.race..wa.H 0.80083 0.14493 0 < 1e-04 ***
## nodefactor.region.EW 0.52492 0.04078 0 < 1e-04 ***
## nodefactor.region.OW 0.14806 0.02260 0 < 1e-04 ***
## concurrent 2.49753 0.06334 0 < 1e-04 ***
## nodematch.race..wa.B -0.58084 0.37503 0 0.121433
## nodematch.race..wa.H -0.31948 0.20560 0 0.120206
## nodematch.race..wa.O 0.53075 0.15580 0 0.000658 ***
## nodematch.region 1.79933 0.05810 0 < 1e-04 ***
## absdiff.sqrt.age -0.54144 0.03277 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Network diagnostics
Model 1
(dx_pers1 <- netdx(est.p.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.5 2056.005 0.019 39.836
## nodefactor.deg.main.1 NA 1847.464 NA 45.857
## nodefactor.race..wa.B NA 251.393 NA 14.955
## nodefactor.race..wa.H NA 442.677 NA 20.121
## nodefactor.region.EW NA 414.225 NA 19.795
## nodefactor.region.OW NA 1343.033 NA 37.362
## concurrent NA 624.816 NA 27.474
## nodematch.race..wa.B NA 7.384 NA 2.804
## nodematch.race..wa.H NA 24.511 NA 5.075
## nodematch.race..wa.O NA 1420.843 NA 32.598
## nodematch.region NA 914.979 NA 29.729
## absdiff.sqrt.age NA 2341.531 NA 60.612
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.549 -0.032 29.975
## Pct Edges Diss 0.032 0.032 0.001 0.004plot(dx_pers1, type="formation")
plot(dx_pers1, type="duration")
plot(dx_pers1, type="dissolution")
Model 2
(dx_pers2 <- netdx(est.p.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2055.780 0.019 40.292
## nodefactor.deg.main.1 NA 1855.445 NA 46.914
## nodefactor.race..wa.B 285.517 290.656 0.018 16.474
## nodefactor.race..wa.H 605.340 612.108 0.011 22.796
## nodefactor.region.EW NA 429.895 NA 18.821
## nodefactor.region.OW NA 1335.726 NA 39.983
## concurrent NA 633.113 NA 28.238
## nodematch.race..wa.B NA 10.435 NA 3.196
## nodematch.race..wa.H NA 45.491 NA 6.134
## nodematch.race..wa.O NA 1251.483 NA 33.221
## nodematch.region NA 908.650 NA 27.993
## absdiff.sqrt.age NA 2337.527 NA 56.329
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.600 -0.031 30.054
## Pct Edges Diss 0.032 0.032 -0.001 0.004plot(dx_pers2, type="formation")
plot(dx_pers2, type="duration")
plot(dx_pers2, type="dissolution")
Model 3
(dx_pers3 <- netdx(est.p.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2056.467 0.019 39.846
## nodefactor.deg.main.1 NA 1857.403 NA 48.986
## nodefactor.race..wa.B 285.517 289.527 0.014 16.377
## nodefactor.race..wa.H 605.340 615.421 0.017 24.201
## nodefactor.region.EW NA 429.632 NA 20.923
## nodefactor.region.OW NA 1336.633 NA 39.472
## concurrent NA 632.565 NA 27.836
## nodematch.race..wa.B 8.480 8.696 0.026 2.844
## nodematch.race..wa.H 51.181 51.117 -0.001 7.010
## nodematch.race..wa.O 1247.081 1272.800 0.021 33.754
## nodematch.region NA 909.946 NA 28.131
## absdiff.sqrt.age NA 2342.339 NA 56.286
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.559 -0.032 30.032
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers3, type="formation")
plot(dx_pers3, type="duration")
plot(dx_pers3, type="dissolution")
Model 4
(dx_pers4 <- netdx(est.p.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2055.367 0.019 37.968
## nodefactor.deg.main.1 1699.000 1731.857 0.019 43.556
## nodefactor.race..wa.B 285.517 291.832 0.022 15.173
## nodefactor.race..wa.H 605.340 614.736 0.016 22.729
## nodefactor.region.EW NA 433.055 NA 18.426
## nodefactor.region.OW NA 1344.062 NA 39.228
## concurrent NA 633.504 NA 26.368
## nodematch.race..wa.B 8.480 8.419 -0.007 2.860
## nodematch.race..wa.H 51.181 52.163 0.019 6.643
## nodematch.race..wa.O 1247.081 1270.809 0.019 32.666
## nodematch.region NA 903.863 NA 26.906
## absdiff.sqrt.age NA 2341.068 NA 55.466
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.549 -0.032 30.018
## Pct Edges Diss 0.032 0.032 0.001 0.004plot(dx_pers4, type="formation")
plot(dx_pers4, type="duration")
plot(dx_pers4, type="dissolution")
Model 5
(dx_pers5 <- netdx(est.p.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2057.617 0.020 41.423
## nodefactor.deg.main.1 1699.000 1732.452 0.020 46.894
## nodefactor.race..wa.B 285.517 290.224 0.016 15.567
## nodefactor.race..wa.H 605.340 618.200 0.021 24.999
## nodefactor.region.EW 367.588 376.614 0.025 19.236
## nodefactor.region.OW 1182.255 1212.373 0.025 37.819
## concurrent NA 640.581 NA 28.028
## nodematch.race..wa.B 8.480 8.806 0.038 3.202
## nodematch.race..wa.H 51.181 52.278 0.021 7.177
## nodematch.race..wa.O 1247.081 1271.794 0.020 34.804
## nodematch.region NA 971.027 NA 28.823
## absdiff.sqrt.age NA 2341.108 NA 61.944
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.649 -0.029 30.129
## Pct Edges Diss 0.032 0.032 -0.002 0.004plot(dx_pers5, type="formation")
plot(dx_pers5, type="duration")
plot(dx_pers5, type="dissolution")
Model 6
(dx_pers6 <- netdx(est.p.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2057.124 0.020 39.201
## nodefactor.deg.main.1 1699.000 1734.663 0.021 44.830
## nodefactor.race..wa.B 285.517 288.638 0.011 15.998
## nodefactor.race..wa.H 605.340 613.457 0.013 24.255
## nodefactor.region.EW 367.588 374.610 0.019 18.334
## nodefactor.region.OW 1182.255 1206.244 0.020 34.054
## concurrent NA 643.826 NA 27.595
## nodematch.race..wa.B 8.480 8.565 0.010 2.669
## nodematch.race..wa.H 51.181 51.686 0.010 7.089
## nodematch.race..wa.O 1247.081 1276.150 0.023 32.213
## nodematch.region NA 972.231 NA 27.787
## absdiff.sqrt.age 1664.841 1693.488 0.017 44.090
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.643 -0.029 30.050
## Pct Edges Diss 0.032 0.032 -0.002 0.004plot(dx_pers6, type="formation")
plot(dx_pers6, type="duration")
plot(dx_pers6, type="dissolution")
Model 7
(dx_pers7 <- netdx(est.p.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2075.818 0.029 70.506
## nodefactor.deg.main.1 1699.000 1765.891 0.039 74.785
## nodefactor.race..wa.B 285.517 286.201 0.002 18.367
## nodefactor.race..wa.H 605.340 592.400 -0.021 28.792
## nodefactor.region.EW 367.588 384.409 0.046 23.763
## nodefactor.region.OW 1182.255 1235.463 0.045 52.008
## concurrent 1384.000 1346.120 -0.027 61.599
## nodematch.race..wa.B 8.480 8.795 0.037 2.753
## nodematch.race..wa.H 51.181 45.461 -0.112 6.771
## nodematch.race..wa.O 1247.081 1304.851 0.046 54.980
## nodematch.region NA 974.307 NA 40.314
## absdiff.sqrt.age 1664.841 1791.730 0.076 71.546
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.557 -0.032 30.108
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers7, type="formation")
plot(dx_pers7, type="duration")
plot(dx_pers7, type="dissolution")
Model 8
(dx_pers8 <- netdx(est.p.buildup.bal[[8]], nsims = 10, nsteps = 1000, ncores = 4))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2004.828 -0.006 61.112
## nodefactor.deg.main.1 1699.000 1701.956 0.002 63.957
## nodefactor.race..wa.B 285.517 277.392 -0.028 19.343
## nodefactor.race..wa.H 605.340 571.014 -0.057 28.804
## nodefactor.region.EW 367.588 346.548 -0.057 27.732
## nodefactor.region.OW 1182.255 1178.426 -0.003 53.673
## concurrent 1384.000 1265.944 -0.085 53.037
## nodematch.race..wa.B 8.480 8.318 -0.019 2.974
## nodematch.race..wa.H 51.181 44.611 -0.128 6.874
## nodematch.race..wa.O 1247.081 1261.513 0.012 48.046
## nodematch.region 1614.000 1528.150 -0.053 50.141
## absdiff.sqrt.age 1664.841 1737.821 0.044 61.666
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.563 -0.032 30.031
## Pct Edges Diss 0.032 0.032 0.001 0.004plot(dx_pers8, type="formation")
plot(dx_pers8, type="duration")
plot(dx_pers8, type="dissolution")